how do speech recognition systems work

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

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Speech recognition software

by Chris Woodford . Last updated: August 17, 2023.

I t's just as well people can understand speech. Imagine if you were like a computer: friends would have to "talk" to you by prodding away at a plastic keyboard connected to your brain by a long, curly wire. If you wanted to say "hello" to someone, you'd have to reach out, chatter your fingers over their keyboard, and wait for their eyes to light up; they'd have to do the same to you. Conversations would be a long, slow, elaborate nightmare—a silent dance of fingers on plastic; strange, abstract, and remote. We'd never put up with such clumsiness as humans, so why do we talk to our computers this way?

Scientists have long dreamed of building machines that can chatter and listen just like humans. But although computerized speech recognition has been around for decades, and is now built into most smartphones and PCs, few of us actually use it. Why? Possibly because we never even bother to try it out, working on the assumption that computers could never pull off a trick so complex as understanding the human voice. It's certainly true that speech recognition is a complex problem that's challenged some of the world's best computer scientists, mathematicians, and linguists. How well are they doing at cracking the problem? Will we all be chatting to our PCs one day soon? Let's take a closer look and find out!

Photo: A court reporter dictates notes into a laptop with a noise-cancelling microphone and speech-recogition software. Photo by Micha Pierce courtesy of US Marine Corps and DVIDS .

What is speech?

Language sets people far above our creeping, crawling animal friends. While the more intelligent creatures, such as dogs and dolphins, certainly know how to communicate with sounds, only humans enjoy the rich complexity of language. With just a couple of dozen letters, we can build any number of words (most dictionaries contain tens of thousands) and express an infinite number of thoughts.

Photo: Speech recognition has been popping up all over the place for quite a few years now. Even my old iPod Touch (dating from around 2012) has a built-in "voice control" program that let you pick out music just by saying "Play albums by U2," or whatever band you're in the mood for.

When we speak, our voices generate little sound packets called phones (which correspond to the sounds of letters or groups of letters in words); so speaking the word cat produces phones that correspond to the sounds "c," "a," and "t." Although you've probably never heard of these kinds of phones before, you might well be familiar with the related concept of phonemes : simply speaking, phonemes are the basic LEGO™ blocks of sound that all words are built from. Although the difference between phones and phonemes is complex and can be very confusing, this is one "quick-and-dirty" way to remember it: phones are actual bits of sound that we speak (real, concrete things), whereas phonemes are ideal bits of sound we store (in some sense) in our minds (abstract, theoretical sound fragments that are never actually spoken).

Computers and computer models can juggle around with phonemes, but the real bits of speech they analyze always involves processing phones. When we listen to speech, our ears catch phones flying through the air and our leaping brains flip them back into words, sentences, thoughts, and ideas—so quickly, that we often know what people are going to say before the words have fully fled from their mouths. Instant, easy, and quite dazzling, our amazing brains make this seem like a magic trick. And it's perhaps because listening seems so easy to us that we think computers (in many ways even more amazing than brains) should be able to hear, recognize, and decode spoken words as well. If only it were that simple!

Why is speech so hard to handle?

The trouble is, listening is much harder than it looks (or sounds): there are all sorts of different problems going on at the same time... When someone speaks to you in the street, there's the sheer difficulty of separating their words (what scientists would call the acoustic signal ) from the background noise —especially in something like a cocktail party, where the "noise" is similar speech from other conversations. When people talk quickly, and run all their words together in a long stream, how do we know exactly when one word ends and the next one begins? (Did they just say "dancing and smile" or "dance, sing, and smile"?) There's the problem of how everyone's voice is a little bit different, and the way our voices change from moment to moment. How do our brains figure out that a word like "bird" means exactly the same thing when it's trilled by a ten year-old girl or boomed by her forty-year-old father? What about words like "red" and "read" that sound identical but mean totally different things (homophones, as they're called)? How does our brain know which word the speaker means? What about sentences that are misheard to mean radically different things? There's the age-old military example of "send reinforcements, we're going to advance" being misheard for "send three and fourpence, we're going to a dance"—and all of us can probably think of song lyrics we've hilariously misunderstood the same way (I always chuckle when I hear Kate Bush singing about "the cattle burning over your shoulder"). On top of all that stuff, there are issues like syntax (the grammatical structure of language) and semantics (the meaning of words) and how they help our brain decode the words we hear, as we hear them. Weighing up all these factors, it's easy to see that recognizing and understanding spoken words in real time (as people speak to us) is an astonishing demonstration of blistering brainpower.

It shouldn't surprise or disappoint us that computers struggle to pull off the same dazzling tricks as our brains; it's quite amazing that they get anywhere near!

Photo: Using a headset microphone like this makes a huge difference to the accuracy of speech recognition: it reduces background sound, making it much easier for the computer to separate the signal (the all-important words you're speaking) from the noise (everything else).

How do computers recognize speech?

Speech recognition is one of the most complex areas of computer science —and partly because it's interdisciplinary: it involves a mixture of extremely complex linguistics, mathematics, and computing itself. If you read through some of the technical and scientific papers that have been published in this area (a few are listed in the references below), you may well struggle to make sense of the complexity. My objective is to give a rough flavor of how computers recognize speech, so—without any apology whatsoever—I'm going to simplify hugely and miss out most of the details.

Broadly speaking, there are four different approaches a computer can take if it wants to turn spoken sounds into written words:

1: Simple pattern matching

Ironically, the simplest kind of speech recognition isn't really anything of the sort. You'll have encountered it if you've ever phoned an automated call center and been answered by a computerized switchboard. Utility companies often have systems like this that you can use to leave meter readings, and banks sometimes use them to automate basic services like balance inquiries, statement orders, checkbook requests, and so on. You simply dial a number, wait for a recorded voice to answer, then either key in or speak your account number before pressing more keys (or speaking again) to select what you want to do. Crucially, all you ever get to do is choose one option from a very short list, so the computer at the other end never has to do anything as complex as parsing a sentence (splitting a string of spoken sound into separate words and figuring out their structure), much less trying to understand it; it needs no knowledge of syntax (language structure) or semantics (meaning). In other words, systems like this aren't really recognizing speech at all: they simply have to be able to distinguish between ten different sound patterns (the spoken words zero through nine) either using the bleeping sounds of a Touch-Tone phone keypad (technically called DTMF ) or the spoken sounds of your voice.

From a computational point of view, there's not a huge difference between recognizing phone tones and spoken numbers "zero", "one," "two," and so on: in each case, the system could solve the problem by comparing an entire chunk of sound to similar stored patterns in its memory. It's true that there can be quite a bit of variability in how different people say "three" or "four" (they'll speak in a different tone, more or less slowly, with different amounts of background noise) but the ten numbers are sufficiently different from one another for this not to present a huge computational challenge. And if the system can't figure out what you're saying, it's easy enough for the call to be transferred automatically to a human operator.

Photo: Voice-activated dialing on cellphones is little more than simple pattern matching. You simply train the phone to recognize the spoken version of a name in your phonebook. When you say a name, the phone doesn't do any particularly sophisticated analysis; it simply compares the sound pattern with ones you've stored previously and picks the best match. No big deal—which explains why even an old phone like this 2001 Motorola could do it.

2: Pattern and feature analysis

Automated switchboard systems generally work very reliably because they have such tiny vocabularies: usually, just ten words representing the ten basic digits. The vocabulary that a speech system works with is sometimes called its domain . Early speech systems were often optimized to work within very specific domains, such as transcribing doctor's notes, computer programming commands, or legal jargon, which made the speech recognition problem far simpler (because the vocabulary was smaller and technical terms were explicitly trained beforehand). Much like humans, modern speech recognition programs are so good that they work in any domain and can recognize tens of thousands of different words. How do they do it?

Most of us have relatively large vocabularies, made from hundreds of common words ("a," "the," "but" and so on, which we hear many times each day) and thousands of less common ones (like "discombobulate," "crepuscular," "balderdash," or whatever, which we might not hear from one year to the next). Theoretically, you could train a speech recognition system to understand any number of different words, just like an automated switchboard: all you'd need to do would be to get your speaker to read each word three or four times into a microphone, until the computer generalized the sound pattern into something it could recognize reliably.

The trouble with this approach is that it's hugely inefficient. Why learn to recognize every word in the dictionary when all those words are built from the same basic set of sounds? No-one wants to buy an off-the-shelf computer dictation system only to find they have to read three or four times through a dictionary, training it up to recognize every possible word they might ever speak, before they can do anything useful. So what's the alternative? How do humans do it? We don't need to have seen every Ford, Chevrolet, and Cadillac ever manufactured to recognize that an unknown, four-wheeled vehicle is a car: having seen many examples of cars throughout our lives, our brains somehow store what's called a prototype (the generalized concept of a car, something with four wheels, big enough to carry two to four passengers, that creeps down a road) and we figure out that an object we've never seen before is a car by comparing it with the prototype. In much the same way, we don't need to have heard every person on Earth read every word in the dictionary before we can understand what they're saying; somehow we can recognize words by analyzing the key features (or components) of the sounds we hear. Speech recognition systems take the same approach.

The recognition process

Practical speech recognition systems start by listening to a chunk of sound (technically called an utterance ) read through a microphone. The first step involves digitizing the sound (so the up-and-down, analog wiggle of the sound waves is turned into digital format, a string of numbers) by a piece of hardware (or software) called an analog-to-digital (A/D) converter (for a basic introduction, see our article on analog versus digital technology ). The digital data is converted into a spectrogram (a graph showing how the component frequencies of the sound change in intensity over time) using a mathematical technique called a Fast Fourier Transform (FFT) ), then broken into a series of overlapping chunks called acoustic frames , each one typically lasting 1/25 to 1/50 of a second. These are digitally processed in various ways and analyzed to find the components of speech they contain. Assuming we've separated the utterance into words, and identified the key features of each one, all we have to do is compare what we have with a phonetic dictionary (a list of known words and the sound fragments or features from which they're made) and we can identify what's probably been said. Probably is always the word in speech recognition: no-one but the speaker can ever know exactly what was said.)

Seeing speech

In theory, since spoken languages are built from only a few dozen phonemes (English uses about 46, while Spanish has only about 24), you could recognize any possible spoken utterance just by learning to pick out phones (or similar key features of spoken language such as formants , which are prominent frequencies that can be used to help identify vowels). Instead of having to recognize the sounds of (maybe) 40,000 words, you'd only need to recognize the 46 basic component sounds (or however many there are in your language), though you'd still need a large phonetic dictionary listing the phonemes that make up each word. This method of analyzing spoken words by identifying phones or phonemes is often called the beads-on-a-string model : a chunk of unknown speech (the string) is recognized by breaking it into phones or bits of phones (the beads); figure out the phones and you can figure out the words.

Most speech recognition programs get better as you use them because they learn as they go along using feedback you give them, either deliberately (by correcting mistakes) or by default (if you don't correct any mistakes, you're effectively saying everything was recognized perfectly—which is also feedback). If you've ever used a program like one of the Dragon dictation systems, you'll be familiar with the way you have to correct your errors straight away to ensure the program continues to work with high accuracy. If you don't correct mistakes, the program assumes it's recognized everything correctly, which means similar mistakes are even more likely to happen next time. If you force the system to go back and tell it which words it should have chosen, it will associate those corrected words with the sounds it heard—and do much better next time.

Screenshot: With speech dictation programs like Dragon NaturallySpeaking, shown here, it's important to go back and correct your mistakes if you want your words to be recognized accurately in future.

3: Statistical analysis

In practice, recognizing speech is much more complex than simply identifying phones and comparing them to stored patterns, and for a whole variety of reasons: Speech is extremely variable: different people speak in different ways (even though we're all saying the same words and, theoretically, they're all built from a standard set of phonemes) You don't always pronounce a certain word in exactly the same way; even if you did, the way you spoke a word (or even part of a word) might vary depending on the sounds or words that came before or after. As a speaker's vocabulary grows, the number of similar-sounding words grows too: the digits zero through nine all sound different when you speak them, but "zero" sounds like "hero," "one" sounds like "none," "two" could mean "two," "to," or "too"... and so on. So recognizing numbers is a tougher job for voice dictation on a PC, with a general 50,000-word vocabulary, than for an automated switchboard with a very specific, 10-word vocabulary containing only the ten digits. The more speakers a system has to recognize, the more variability it's going to encounter and the bigger the likelihood of making mistakes. For something like an off-the-shelf voice dictation program (one that listens to your voice and types your words on the screen), simple pattern recognition is clearly going to be a bit hit and miss. The basic principle of recognizing speech by identifying its component parts certainly holds good, but we can do an even better job of it by taking into account how language really works. In other words, we need to use what's called a language model .

When people speak, they're not simply muttering a series of random sounds. Every word you utter depends on the words that come before or after. For example, unless you're a contrary kind of poet, the word "example" is much more likely to follow words like "for," "an," "better," "good", "bad," and so on than words like "octopus," "table," or even the word "example" itself. Rules of grammar make it unlikely that a noun like "table" will be spoken before another noun ("table example" isn't something we say) while—in English at least—adjectives ("red," "good," "clear") come before nouns and not after them ("good example" is far more probable than "example good"). If a computer is trying to figure out some spoken text and gets as far as hearing "here is a ******* example," it can be reasonably confident that ******* is an adjective and not a noun. So it can use the rules of grammar to exclude nouns like "table" and the probability of pairs like "good example" and "bad example" to make an intelligent guess. If it's already identified a "g" sound instead of a "b", that's an added clue.

Virtually all modern speech recognition systems also use a bit of complex statistical hocus-pocus to help figure out what's being said. The probability of one phone following another, the probability of bits of silence occurring in between phones, and the likelihood of different words following other words are all factored in. Ultimately, the system builds what's called a hidden Markov model (HMM) of each speech segment, which is the computer's best guess at which beads are sitting on the string, based on all the things it's managed to glean from the sound spectrum and all the bits and pieces of phones and silence that it might reasonably contain. It's called a Markov model (or Markov chain), for Russian mathematician Andrey Markov , because it's a sequence of different things (bits of phones, words, or whatever) that change from one to the next with a certain probability. Confusingly, it's referred to as a "hidden" Markov model even though it's worked out in great detail and anything but hidden! "Hidden," in this case, simply means the contents of the model aren't observed directly but figured out indirectly from the sound spectrum. From the computer's viewpoint, speech recognition is always a probabilistic "best guess" and the right answer can never be known until the speaker either accepts or corrects the words that have been recognized. (Markov models can be processed with an extra bit of computer jiggery pokery called the Viterbi algorithm , but that's beyond the scope of this article.)

4: Artificial neural networks

HMMs have dominated speech recognition since the 1970s—for the simple reason that they work so well. But they're by no means the only technique we can use for recognizing speech. There's no reason to believe that the brain itself uses anything like a hidden Markov model. It's much more likely that we figure out what's being said using dense layers of brain cells that excite and suppress one another in intricate, interlinked ways according to the input signals they receive from our cochleas (the parts of our inner ear that recognize different sound frequencies).

Back in the 1980s, computer scientists developed "connectionist" computer models that could mimic how the brain learns to recognize patterns, which became known as artificial neural networks (sometimes called ANNs). A few speech recognition scientists explored using neural networks, but the dominance and effectiveness of HMMs relegated alternative approaches like this to the sidelines. More recently, scientists have explored using ANNs and HMMs side by side and found they give significantly higher accuracy over HMMs used alone.

Artwork: Neural networks are hugely simplified, computerized versions of the brain—or a tiny part of it that have inputs (where you feed in information), outputs (where results appear), and hidden units (connecting the two). If you train them with enough examples, they learn by gradually adjusting the strength of the connections between the different layers of units. Once a neural network is fully trained, if you show it an unknown example, it will attempt to recognize what it is based on the examples it's seen before.

Speech recognition: a summary

Artwork: A summary of some of the key stages of speech recognition and the computational processes happening behind the scenes.

What can we use speech recognition for?

We've already touched on a few of the more common applications of speech recognition, including automated telephone switchboards and computerized voice dictation systems. But there are plenty more examples where those came from.

Many of us (whether we know it or not) have cellphones with voice recognition built into them. Back in the late 1990s, state-of-the-art mobile phones offered voice-activated dialing , where, in effect, you recorded a sound snippet for each entry in your phonebook, such as the spoken word "Home," or whatever that the phone could then recognize when you spoke it in future. A few years later, systems like SpinVox became popular helping mobile phone users make sense of voice messages by converting them automatically into text (although a sneaky BBC investigation eventually claimed that some of its state-of-the-art speech automated speech recognition was actually being done by humans in developing countries!).

Today's smartphones make speech recognition even more of a feature. Apple's Siri , Google Assistant ("Hey Google..."), and Microsoft's Cortana are smartphone "personal assistant apps" who'll listen to what you say, figure out what you mean, then attempt to do what you ask, whether it's looking up a phone number or booking a table at a local restaurant. They work by linking speech recognition to complex natural language processing (NLP) systems, so they can figure out not just what you say , but what you actually mean , and what you really want to happen as a consequence. Pressed for time and hurtling down the street, mobile users theoretically find this kind of system a boon—at least if you believe the hype in the TV advertisements that Google and Microsoft have been running to promote their systems. (Google quietly incorporated speech recognition into its search engine some time ago, so you can Google just by talking to your smartphone, if you really want to.) If you have one of the latest voice-powered electronic assistants, such as Amazon's Echo/Alexa or Google Home, you don't need a computer of any kind (desktop, tablet, or smartphone): you just ask questions or give simple commands in your natural language to a thing that resembles a loudspeaker ... and it answers straight back.

Screenshot: When I asked Google "does speech recognition really work," it took it three attempts to recognize the question correctly.

Will speech recognition ever take off?

I'm a huge fan of speech recognition. After suffering with repetitive strain injury on and off for some time, I've been using computer dictation to write quite a lot of my stuff for about 15 years, and it's been amazing to see the improvements in off-the-shelf voice dictation over that time. The early Dragon NaturallySpeaking system I used on a Windows 95 laptop was fairly reliable, but I had to speak relatively slowly, pausing slightly between each word or word group, giving a horribly staccato style that tended to interrupt my train of thought. This slow, tedious one-word-at-a-time approach ("can – you – tell – what – I – am – saying – to – you") went by the name discrete speech recognition . A few years later, things had improved so much that virtually all the off-the-shelf programs like Dragon were offering continuous speech recognition , which meant I could speak at normal speed, in a normal way, and still be assured of very accurate word recognition. When you can speak normally to your computer, at a normal talking pace, voice dictation programs offer another advantage: they give clumsy, self-conscious writers a much more attractive, conversational style: "write like you speak" (always a good tip for writers) is easy to put into practice when you speak all your words as you write them!

Despite the technological advances, I still generally prefer to write with a keyboard and mouse . Ironically, I'm writing this article that way now. Why? Partly because it's what I'm used to. I often write highly technical stuff with a complex vocabulary that I know will defeat the best efforts of all those hidden Markov models and neural networks battling away inside my PC. It's easier to type "hidden Markov model" than to mutter those words somewhat hesitantly, watch "hiccup half a puddle" pop up on screen and then have to make corrections.

Screenshot: You an always add more words to a speech recognition program. Here, I've decided to train the Microsoft Windows built-in speech recognition engine to spot the words 'hidden Markov model.'

Mobile revolution?

You might think mobile devices—with their slippery touchscreens —would benefit enormously from speech recognition: no-one really wants to type an essay with two thumbs on a pop-up QWERTY keyboard. Ironically, mobile devices are heavily used by younger, tech-savvy kids who still prefer typing and pawing at screens to speaking out loud. Why? All sorts of reasons, from sheer familiarity (it's quick to type once you're used to it—and faster than fixing a computer's goofed-up guesses) to privacy and consideration for others (many of us use our mobile phones in public places and we don't want our thoughts wide open to scrutiny or howls of derision), and the sheer difficulty of speaking clearly and being clearly understood in noisy environments. Recently, I was walking down a street and overheard a small garden party where the sounds of happy laughter, drinking, and discreet background music were punctuated by a sudden grunt of "Alexa play Copacabana by Barry Manilow"—which silenced the conversation entirely and seemed jarringly out of place. Speech recognition has never been so indiscreet. What you're doing with your computer also makes a difference. If you've ever used speech recognition on a PC, you'll know that writing something like an essay (dictating hundreds or thousands of words of ordinary text) is a whole lot easier than editing it afterwards (where you laboriously try to select words or sentences and move them up or down so many lines with awkward cut and paste commands). And trying to open and close windows, start programs, or navigate around a computer screen by voice alone is clumsy, tedious, error-prone, and slow. It's far easier just to click your mouse or swipe your finger.

Photo: Here I'm using Google's Live Transcribe app to dictate the last paragraph of this article. As you can see, apart from the punctuation, the transcription is flawless, without any training at all. This is the fastest and most accurate speech recognition software I've ever used. It's mainly designed as an accessibility aid for deaf and hard of hearing people, but it can be used for dictation too.

Developers of speech recognition systems insist everything's about to change, largely thanks to natural language processing and smart search engines that can understand spoken queries. ("OK Google...") But people have been saying that for decades now: the brave new world is always just around the corner. According to speech pioneer James Baker, better speech recognition "would greatly increase the speed and ease with which humans could communicate with computers, and greatly speed and ease the ability with which humans could record and organize their own words and thoughts"—but he wrote (or perhaps voice dictated?) those words 25 years ago! Just because Google can now understand speech, it doesn't follow that we automatically want to speak our queries rather than type them—especially when you consider some of the wacky things people look for online. Humans didn't invent written language because others struggled to hear and understand what they were saying. Writing and speaking serve different purposes. Writing is a way to set out longer, more clearly expressed and elaborated thoughts without having to worry about the limitations of your short-term memory; speaking is much more off-the-cuff. Writing is grammatical; speech doesn't always play by the rules. Writing is introverted, intimate, and inherently private; it's carefully and thoughtfully composed. Speaking is an altogether different way of expressing your thoughts—and people don't always want to speak their minds. While technology may be ever advancing, it's far from certain that speech recognition will ever take off in quite the way that its developers would like. I'm typing these words, after all, not speaking them.

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Find out more, on this website.

• Microphones
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• Automatic Speech Recognition: A Deep Learning Approach by Dong Yu and Li Deng. Springer, 2015. Two Microsoft researchers review state-of-the-art, neural-network approaches to recognition.
• Theory and Applications of Digital Speech Processing by Lawrence R. Rabiner and Ronald W. Schafer. Pearson, 2011. An up-to-date review at undergraduate level.
• Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Daniel Jurafsky, James Martin. Prentice Hall, 2009. An up-to-date, interdisciplinary review of speech recognition technology.
• Statistical Methods for Speech Recognition by Frederick Jelinek. MIT Press, 1997. A detailed guide to Hidden Markov Models and the other statistical techniques that computers use to figure out human speech.
• Fundamentals of Speech Recognition by Lawrence R. Rabiner and Biing-Hwang Juang. PTR Prentice Hall, 1993. A little dated now, but still a good introduction to the basic concepts.
• Speech Recognition: Invited Papers Presented at the 1974 IEEE Symposium by D. R. Reddy (ed). Academic Press, 1975. A classic collection of pioneering papers from the golden age of the 1970s.

Easy-to-understand

• Lost voices, ignored words: Apple's speech recognition needs urgent reform by Colin Hughes, The Register, 16 August 2023. How speech recognition software ignores the needs of the people who need it most—disabled people with different accessibility needs.
• Android's Live Transcribe will let you save transcriptions and show 'sound events' by Dieter Bohn, The Verge, 16 May 2019. An introduction to Google's handy, 70-language transcription app.
• Hey, Siri: Read My Lips by Emily Waltz, IEEE Spectrum, 8 February 2019. How your computer can translate your words... without even listening.
• Interpol's New Software Will Recognize Criminals by Their Voices by Michael Dumiak, 16 May 2018. Is it acceptable for law enforcement agencies to store huge quantities of our voice samples if it helps them trap the occasional bad guy?
• Cypher: The Deep-Learning Software That Will Help Siri, Alexa, and Cortana Hear You : by Amy Nordrum. IEEE Spectrum, 24 October 2016. Cypher helps voice recognition programs to separate speech signals from background noise.
• In the Future, How Will We Talk to Our Technology? : by David Pierce. Wired, 27 September 2015. What sort of hardware will we use with future speech recognition software?
• The Holy Grail of Speech Recognition by Janie Chang: Microsoft Research, 29 August 2011. How neural networks are making a comeback in speech recognition research. [Archived via the Wayback Machine.]
• Audio Alchemy: Getting Computers to Understand Overlapping Speech by John R. Hershey et al. Scientific American, April 12, 2011. How can computers make sense of two people talking at once?
• How Siri Works: Interview with Tom Gruber by Nova Spivack, Minding the Planet, 26 January 2010. Gruber explains some of the technical tricks that allow Siri to understand natural language.
• A sound start for speech tech : by LJ Rich. BBC News, 15 May 2009. Cambridge University's Dr Tony Robinson talks us through the science of speech recognition.
• Speech Recognition by Computer by Stephen E. Levinson and Mark Y. Liberman, Scientific American, Vol. 244, No. 4 (April 1981), pp. 64–77. A more detailed overview of the basic concepts. A good article to continue with after you've read mine.

More technical

• An All-Neural On-Device Speech Recognizer by Johan Schalkwyk, Google AI Blog, March 12, 2019. Google announces a state-of-the-art speech recognition system based entirely on what are called recurrent neural network transducers (RNN-Ts).
• Improving End-to-End Models For Speech Recognition by Tara N. Sainath, and Yonghui Wu, Google Research Blog, December 14, 2017. A cutting-edge speech recognition model that integrates traditionally separate aspects of speech recognition into a single system.
• A Historical Perspective of Speech Recognition by Xuedong Huang, James Baker, Raj Reddy. Communications of the ACM, January 2014 (Vol. 57 No. 1), Pages 94–103.
• [PDF] Application Of Pretrained Deep Neural Networks To Large Vocabulary Speech Recognition by Navdeep Jaitly, Patrick Nguyen, Andrew Senior, Vincent Vanhoucke. Proceedings of Interspeech 2012. An insight into Google's use of neural networks for speech recognition.
• Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition by George Dahl et al. IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20 No. 1, January 2012. A review of Microsoft's recent research into using neural networks with HMMs.
• Speech Recognition Technology: A Critique by Stephen E. Levinson, Proceedings of the National Academy of Sciences of the United States of America. Vol. 92, No. 22, October 24, 1995, pp. 9953–9955.
• Hidden Markov Models for Speech Recognition by B. H. Juang and L. R. Rabiner, Technometrics, Vol. 33, No. 3, August, 1991, pp. 251–272.
• A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition by Lawrence R. Rabiner. Proceedings of the IEEE, Vol 77 No 2, February 1989. A classic introduction to Markov models, though non-mathematicians will find it tough going.
• US Patent: 4,783,803: Speech recognition apparatus and method by James K. Baker, Dragon Systems, 8 November 1988. One of Baker's first Dragon patents. Another Baker patent filed the following year follows on from this. See US Patent: 4,866,778: Interactive speech recognition apparatus by James K. Baker, Dragon Systems, 12 September 1989.
• US Patent 4,783,804: Hidden Markov model speech recognition arrangement by Stephen E. Levinson, Lawrence R. Rabiner, and Man M. Sondi, AT&T Bell Laboratories, 6 May 1986. Sets out one approach to probabilistic speech recognition using Markov models.
• US Patent: 4,363,102: Speaker identification system using word recognition templates by John E. Holmgren, Bell Labs, 7 December 1982. A method of recognizing a particular person's voice using analysis of key features.
• US Patent 2,938,079: Spectrum segmentation system for the automatic extraction of formant frequencies from human speech by James L. Flanagan, US Air Force, 24 May 1960. An early speech recognition system based on formant (peak frequency) analysis.
• A Historical Perspective of Speech Recognition by Raj Reddy (an AI researcher at Carnegie Mellon), James Baker (founder of Dragon), and Xuedong Huang (of Microsoft). Speech recognition pioneers look back on the advances they helped to inspire in this four-minute discussion.

Text copyright © Chris Woodford 2007, 2020. All rights reserved. Full copyright notice and terms of use .

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Speech Recognition: Everything You Need to Know in 2024

Speech recognition, also known as automatic speech recognition (ASR) , enables seamless communication between humans and machines. This technology empowers organizations to transform human speech into written text. Speech recognition technology can revolutionize many business applications , including customer service, healthcare, finance and sales.

In this comprehensive guide, we will explain speech recognition, exploring how it works, the algorithms involved, and the use cases of various industries.

If you require training data for your speech recognition system, here is a guide to finding the right speech data collection services.

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text.

Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents, dialects, and speech patterns.

What are the features of speech recognition systems?

Speech recognition systems have several components that work together to understand and process human speech. Key features of effective speech recognition are:

• Audio preprocessing: After you have obtained the raw audio signal from an input device, you need to preprocess it to improve the quality of the speech input The main goal of audio preprocessing is to capture relevant speech data by removing any unwanted artifacts and reducing noise.
• Feature extraction: This stage converts the preprocessed audio signal into a more informative representation. This makes raw audio data more manageable for machine learning models in speech recognition systems.
• Language model weighting: Language weighting gives more weight to certain words and phrases, such as product references, in audio and voice signals. This makes those keywords more likely to be recognized in a subsequent speech by speech recognition systems.
• Acoustic modeling : It enables speech recognizers to capture and distinguish phonetic units within a speech signal. Acoustic models are trained on large datasets containing speech samples from a diverse set of speakers with different accents, speaking styles, and backgrounds.
• Speaker labeling: It enables speech recognition applications to determine the identities of multiple speakers in an audio recording. It assigns unique labels to each speaker in an audio recording, allowing the identification of which speaker was speaking at any given time.
• Profanity filtering: The process of removing offensive, inappropriate, or explicit words or phrases from audio data.

What are the different speech recognition algorithms?

Speech recognition uses various algorithms and computation techniques to convert spoken language into written language. The following are some of the most commonly used speech recognition methods:

• Hidden Markov Models (HMMs): Hidden Markov model is a statistical Markov model commonly used in traditional speech recognition systems. HMMs capture the relationship between the acoustic features and model the temporal dynamics of speech signals.
• Estimate the probability of word sequences in the recognized text
• Convert colloquial expressions and abbreviations in a spoken language into a standard written form
• Map phonetic units obtained from acoustic models to their corresponding words in the target language.
• Speaker Diarization (SD): Speaker diarization, or speaker labeling, is the process of identifying and attributing speech segments to their respective speakers (Figure 1). It allows for speaker-specific voice recognition and the identification of individuals in a conversation.

Figure 1: A flowchart illustrating the speaker diarization process

• Dynamic Time Warping (DTW): Speech recognition algorithms use Dynamic Time Warping (DTW) algorithm to find an optimal alignment between two sequences (Figure 2).

Figure 2: A speech recognizer using dynamic time warping to determine the optimal distance between elements

5. Deep neural networks: Neural networks process and transform input data by simulating the non-linear frequency perception of the human auditory system.

6. Connectionist Temporal Classification (CTC): It is a training objective introduced by Alex Graves in 2006. CTC is especially useful for sequence labeling tasks and end-to-end speech recognition systems. It allows the neural network to discover the relationship between input frames and align input frames with output labels.

Speech recognition vs voice recognition

Speech recognition is commonly confused with voice recognition, yet, they refer to distinct concepts. Speech recognition converts  spoken words into written text, focusing on identifying the words and sentences spoken by a user, regardless of the speaker’s identity. 

On the other hand, voice recognition is concerned with recognizing or verifying a speaker’s voice, aiming to determine the identity of an unknown speaker rather than focusing on understanding the content of the speech.

What are the challenges of speech recognition with solutions?

While speech recognition technology offers many benefits, it still faces a number of challenges that need to be addressed. Some of the main limitations of speech recognition include:

Acoustic Challenges:

• Assume a speech recognition model has been primarily trained on American English accents. If a speaker with a strong Scottish accent uses the system, they may encounter difficulties due to pronunciation differences. For example, the word “water” is pronounced differently in both accents. If the system is not familiar with this pronunciation, it may struggle to recognize the word “water.”

Solution: Addressing these challenges is crucial to enhancing  speech recognition applications’ accuracy. To overcome pronunciation variations, it is essential to expand the training data to include samples from speakers with diverse accents. This approach helps the system recognize and understand a broader range of speech patterns.

• For instance, you can use data augmentation techniques to reduce the impact of noise on audio data. Data augmentation helps train speech recognition models with noisy data to improve model accuracy in real-world environments.

Figure 3: Examples of a target sentence (“The clown had a funny face”) in the background noise of babble, car and rain.

Linguistic Challenges:

• Out-of-vocabulary words: Since the speech recognizers model has not been trained on OOV words, they may incorrectly recognize them as different or fail to transcribe them when encountering them.

Figure 4: An example of detecting OOV word

Solution: Word Error Rate (WER) is a common metric that is used to measure the accuracy of a speech recognition or machine translation system. The word error rate can be computed as:

Figure 5: Demonstrating how to calculate word error rate (WER)

• Homophones: Homophones are words that are pronounced identically but have different meanings, such as “to,” “too,” and “two”. Solution: Semantic analysis allows speech recognition programs to select the appropriate homophone based on its intended meaning in a given context. Addressing homophones improves the ability of the speech recognition process to understand and transcribe spoken words accurately.

Technical/System Challenges:

• Data privacy and security: Speech recognition systems involve processing and storing sensitive and personal information, such as financial information. An unauthorized party could use the captured information, leading to privacy breaches.

Solution: You can encrypt sensitive and personal audio information transmitted between the user’s device and the speech recognition software. Another technique for addressing data privacy and security in speech recognition systems is data masking. Data masking algorithms mask and replace sensitive speech data with structurally identical but acoustically different data.

Figure 6: An example of how data masking works

• Limited training data: Limited training data directly impacts  the performance of speech recognition software. With insufficient training data, the speech recognition model may struggle to generalize different accents or recognize less common words.

Solution: To improve the quality and quantity of training data, you can expand the existing dataset using data augmentation and synthetic data generation technologies.

13 speech recognition use cases and applications

In this section, we will explain how speech recognition revolutionizes the communication landscape across industries and changes the way businesses interact with machines.

Customer Service and Support

• Interactive Voice Response (IVR) systems: Interactive voice response (IVR) is a technology that automates the process of routing callers to the appropriate department. It understands customer queries and routes calls to the relevant departments. This reduces the call volume for contact centers and minimizes wait times. IVR systems address simple customer questions without human intervention by employing pre-recorded messages or text-to-speech technology . Automatic Speech Recognition (ASR) allows IVR systems to comprehend and respond to customer inquiries and complaints in real time.
• Customer support automation and chatbots: According to a survey, 78% of consumers interacted with a chatbot in 2022, but 80% of respondents said using chatbots increased their frustration level.
• Sentiment analysis and call monitoring: Speech recognition technology converts spoken content from a call into text. After  speech-to-text processing, natural language processing (NLP) techniques analyze the text and assign a sentiment score to the conversation, such as positive, negative, or neutral. By integrating speech recognition with sentiment analysis, organizations can address issues early on and gain valuable insights into customer preferences.
• Multilingual support: Speech recognition software can be trained in various languages to recognize and transcribe the language spoken by a user accurately. By integrating speech recognition technology into chatbots and Interactive Voice Response (IVR) systems, organizations can overcome language barriers and reach a global audience (Figure 7). Multilingual chatbots and IVR automatically detect the language spoken by a user and switch to the appropriate language model.

Figure 7: Showing how a multilingual chatbot recognizes words in another language

• Customer authentication with voice biometrics: Voice biometrics use speech recognition technologies to analyze a speaker’s voice and extract features such as accent and speed to verify their identity.

Sales and Marketing:

• Virtual sales assistants: Virtual sales assistants are AI-powered chatbots that assist customers with purchasing and communicate with them through voice interactions. Speech recognition allows virtual sales assistants to understand the intent behind spoken language and tailor their responses based on customer preferences.
• Transcription services : Speech recognition software records audio from sales calls and meetings and then converts the spoken words into written text using speech-to-text algorithms.

Automotive:

• Voice-activated controls: Voice-activated controls allow users to interact with devices and applications using voice commands. Drivers can operate features like climate control, phone calls, or navigation systems.
• Voice-assisted navigation: Voice-assisted navigation provides real-time voice-guided directions by utilizing the driver’s voice input for the destination. Drivers can request real-time traffic updates or search for nearby points of interest using voice commands without physical controls.

Healthcare:

• Recording the physician’s dictation
• Transcribing the audio recording into written text using speech recognition technology
• Editing the transcribed text for better accuracy and correcting errors as needed
• Formatting the document in accordance with legal and medical requirements.
• Virtual medical assistants: Virtual medical assistants (VMAs) use speech recognition, natural language processing, and machine learning algorithms to communicate with patients through voice or text. Speech recognition software allows VMAs to respond to voice commands, retrieve information from electronic health records (EHRs) and automate the medical transcription process.
• Electronic Health Records (EHR) integration: Healthcare professionals can use voice commands to navigate the EHR system , access patient data, and enter data into specific fields.

Technology:

• Virtual agents: Virtual agents utilize natural language processing (NLP) and speech recognition technologies to understand spoken language and convert it into text. Speech recognition enables virtual agents to process spoken language in real-time and respond promptly and accurately to user voice commands.

Further reading

• Top 5 Speech Recognition Data Collection Methods in 2023
• Top 11 Speech Recognition Applications in 2023

External Links

• 1. Databricks
• 2. PubMed Central
• 3. Qin, L. (2013). Learning Out-of-vocabulary Words in Automatic Speech Recognition . Carnegie Mellon University.
• 4. Wikipedia

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Top 11 Voice Recognition Applications in 2024

How Does Voice Recognition Work?

We use voice recognition all the time, but how does it work?

Sometimes, we find ourselves speaking to our digital devices more than other people. The digital assistants on our devices use voice recognition to understand what we're saying. Because of this, we're able to manage many aspects of our lives just by having a conversation with our phone or smart speaker.

Even though voice recognition is such a large part of our lives, we don't usually think about what makes it work. A lot goes on behind the scenes with voice recognition, so here's a dive into what makes it work.

What Is Voice Recognition?

Modern devices usually come loaded with a digital assistant, a program that uses voice recognition to carry out certain tasks on your device. Voice recognition is a set of algorithms that the assistants use to convert your speech into a digital signal and ascertain what you're saying. Programs like Microsoft Word use voice recognition to help type down words.

The First Voice Recognition System

The first voice recognition system was called the Audrey system. The name was a contraction of "Automated Digit Recognition." Invented in 1952 by Bell Laboratories, Audrey was able to recognize numerical digits. The speaker would say a number, and Audrey would light up one of 10 corresponding lightbulbs.

As groundbreaking as this invention was, it wasn't well received. The computer system itself stood about six feet tall and took up a massive amount of space. Regardless of its size, it could only decipher numbers 0-9. Also, only a person with a specific type of voice could use Audrey, so it was manned primarily by one person.

While it had its faults, Audrey was the first step in a long journey to make voice recognition what it is today. It didn't take long before the next voice recognition system arose, which could understand sequences of words.

Related: How to Lock/Unlock an Android Phone With Your Voice Using Google Assistant

Voice Recognition Begins With Converting the Audio Into a Digital Signal

Voice recognition systems have to go through certain steps to figure out what we're saying. When your device's microphone picks up your audio, it's converted into an electrical current which travels down to the Analog to Digital Converter (ADC). As the name suggests, the ADC converts the electric current (AKA, the analog signal) into a digital binary signal.

As the current flows to the ADC, it takes samples of the current and deciphers its voltage at certain points in time. The voltage at a given point in time is called a sample. Each sample is only several thousandths of a second long. Based on the sample's voltage, the ADC will assign a series of eight binary digits (one byte of data).

The Audio Is Processed for Clarity

In order for the device to better understand the speaker, the audio needs to be processed to improve clarity. The device is sometimes tasked with deciphering speech in a noisy environment; thus, certain filters are placed on the audio to help eliminate background noise. For some voice recognition systems, frequencies that are higher and lower than the human's hearing range are filtered out.

The system doesn't only get rid of unwanted frequencies; certain frequencies in the audio are also emphasized so that the computer can better recognize the voice and separate it from background noise. Some voice recognition systems actually split the audio up into several discrete frequencies.

Related: How to Teach Google Assistant to Pronounce Your Name Correctly

Other aspects, such as the speed and volume of the audio, are adjusted to better match the references audio samples that the voice recognition system uses to compare. These filtration and denoising processes really help improve the overall accuracy.

The Voice Recognition System Then Starts Making Words

There are two popular ways that voice recognition systems analyze speech. One is called the hidden Markov model, and the other method is through neural networks.

The Hidden Markov Model Method

The hidden Markov model is the method employed in most voice recognition systems. An important part of this process is breaking down the spoken words into their phonemes (the smallest element of a language). There's a finite number of phonemes in each language, which is why the hidden Markov model method works so well.

There are around 40 phonemes in the English language. When the voice recognition system identifies one, it determines the probability of what the next one will be.

For example, if the speaker utters the sound "ta," there's a certain probability that the next phoneme will be "p" to form the word "tap." There's also the probability that the next phoneme will be "s," but that's far less likely. If the next phoneme does resemble "p," then the system can assume with high certainty that the word is "tap."

The Neural Network Method

A neural network is like a digital brain that learns much in the same way that a human brain does. Neural networks are instrumental in the progress of artificial intelligence and deep learning.

The type of neural network that voice recognition uses is called a Recurrent Neural Network (RNN). According to GeeksforGeeks , RNN is one where the "output from [the] previous step[s] are fed as input to the current step." This means that when an RNN processes a bit of data, it uses that data to influence what it does with the next bit of data— it essentially learns from experience.

The more an RNN is exposed to a certain language, the more accurate the voice recognition will be. If the system identifies the "ta" sound 100 times, and it's followed by the "p" sound 90 of those times, then the network can basically learn that "p" typically comes after "ta."

Because of this, when the voice recognition system identifies a phoneme, it uses the accrued data to predict which one will likely come next. Because RNNs continuously learn, the more it's used, the more accurate the voice recognition will be.

After the voice recognition system identifies the words (whether with the hidden Marvok model or with an RNN), that information is sent to the processor. The system then carries out the task that it's meant to do.

Voice Recognition Has Become a Staple in Modern Technology

Voice recognition has become a huge part of our modern technological landscape. It's been implemented into several industries and services worldwide; indeed, many people control their entire lives with voice-activated assistants. You can find assistants like Siri loaded onto your Apple watches. What was only a dream back in 1952 has become a reality, and it doesn't seem to be stopping anytime soon.

Essential Guide to Automatic Speech Recognition Technology

Over the past decade, AI-powered speech recognition systems have slowly become part of our everyday lives, from voice search to virtual assistants in contact centers, cars, hospitals, and restaurants. These speech recognition developments are made possible by deep learning advancements.

Developers across many industries now use automatic speech recognition (ASR) to increase business productivity, application efficiency, and even digital accessibility. This post discusses ASR, how it works, use cases, advancements, and more.

What is automatic speech recognition?

Speech recognition technology is capable of converting spoken language (an audio signal) into written text that is often used as a command.

Today’s most advanced software can accurately process varying language dialects and accents. For example, ASR is commonly seen in user-facing applications such as virtual agents, live captioning, and clinical note-taking. Accurate speech transcription is essential for these use cases.

Developers in the speech AI space also use  alternative terminologies  to describe speech recognition such as ASR, speech-to-text (STT), and voice recognition.

ASR is a critical component of  speech AI , which is a suite of technologies designed to help humans converse with computers through voice.

Why natural language processing is used in speech recognition

Developers are often unclear about the role of natural language processing (NLP) models in the ASR pipeline. Aside from being applied in language models, NLP is also used to augment generated transcripts with punctuation and capitalization at the end of the ASR pipeline.

After the transcript is post-processed with NLP, the text is used for downstream language modeling tasks:

• Sentiment analysis
• Text analytics
• Text summarization
• Question answering

Speech recognition algorithms

Speech recognition algorithms can be implemented in a traditional way using statistical algorithms or by using deep learning techniques such as neural networks to convert speech into text.

Traditional ASR algorithms

Hidden Markov models (HMM) and dynamic time warping (DTW) are two such examples of traditional statistical techniques for performing speech recognition.

Using a set of transcribed audio samples, an HMM is trained to predict word sequences by varying the model parameters to maximize the likelihood of the observed audio sequence.

DTW is a dynamic programming algorithm that finds the best possible word sequence by calculating the distance between time series: one representing the unknown speech and others representing the known words.

Deep learning ASR algorithms

For the last few years, developers have been interested in deep learning for speech recognition because statistical algorithms are less accurate. In fact, deep learning algorithms work better at understanding dialects, accents, context, and multiple languages, and they transcribe accurately even in noisy environments.

Some of the most popular state-of-the-art speech recognition acoustic models are Quartznet , Citrinet , and Conformer . In a typical speech recognition pipeline, you can choose and switch any acoustic model that you want based on your use case and performance.

Implementation tools for deep learning models

Several tools are available for developing deep learning speech recognition models and pipelines, including Kaldi , Mozilla DeepSpeech, NVIDIA NeMo , NVIDIA Riva , NVIDIA TAO Toolkit , and services from Google, Amazon, and Microsoft.

Kaldi, DeepSpeech, and NeMo are open-source toolkits that help you build speech recognition models. TAO Toolkit and Riva are closed-source SDKs that help you develop customizable pipelines that can be deployed in production.

Cloud service providers like Google, AWS, and Microsoft offer generic services that you can easily plug and play with.

Deep learning speech recognition pipeline

An ASR pipeline consists of the following components:

• Spectrogram generator that converts raw audio to spectrograms.
• Acoustic model that takes the spectrograms as input and outputs a matrix of probabilities over characters over time.
• Decoder (optionally coupled with a language model) that generates possible sentences from the probability matrix.
• Punctuation and capitalization model that formats the generated text for easier human consumption.

A typical deep learning pipeline for speech recognition includes the following components:

• Data preprocessing
• Neural acoustic model
• Decoder (optionally coupled with an n-gram language model)
• Punctuation and capitalization model

Figure 1 shows an example of a deep learning speech recognition pipeline:.

Datasets are essential in any deep learning application. Neural networks function similarly to the human brain. The more data you use to teach the model, the more it learns. The same is true for the speech recognition pipeline.

A few popular speech recognition datasets are

• LibriSpeech
• Fisher English Training Speech
• Mozilla Common Voice (MCV)
• 2000 HUB 5 English Evaluation Speech
• AN4 (includes recordings of people spelling out addresses and names)
• Aishell-1/AIshell-2 Mandarin speech corpus

Data processing is the first step. It includes data preprocessing and augmentation techniques such as speed/time/noise/impulse perturbation and time stretch augmentation, fast Fourier Transformations (FFT) using windowing, and normalization techniques.

For example, in Figure 2, the mel spectrogram is generated from a raw audio waveform after applying FFT using the windowing technique.

We can also use perturbation techniques to augment the training dataset. Figures 3 and 4 represent techniques like noise perturbation and masking being used to increase the size of the training dataset in order to avoid problems like overfitting.

The output of the data preprocessing stage is a spectrogram/mel spectrogram, which is a visual representation of the strength of the audio signal over time. 

Mel spectrograms are then fed into the next stage: a neural acoustic model . QuartzNet, CitriNet, ContextNet, Conformer-CTC, and Conformer-Transducer are examples of cutting-edge neural acoustic models. Multiple ASR models exist for several reasons, such as the need for real-time performance, higher accuracy, memory size, and compute cost for your use case.

However, Conformer-based models are becoming more popular due to their improved accuracy and ability to comprehend. The acoustic model returns the probability of characters/words at each time stamp.

Figure 5 shows the output of the acoustic model, with time stamps. 

The acoustic model’s output is fed into the decoder along with the language model. Decoders include beam search and greedy decoders, and language models include n-gram language, KenLM, and neural scoring. When it comes to the decoder, it helps to generate top words, which are then passed to language models to predict the correct sentence.

In Figure 6, the decoder selects the next best word based on the probability score. Based on the final highest score, the correct word or sentence is selected and sent to the punctuation and capitalization model.

The ASR pipeline generates text with no punctuation or capitalization.

Finally, a punctuation and capitalization model is used to improve the text quality for better readability. Bidirectional Encoder Representations from Transformers (BERT) models are commonly used to generate punctuated text.

Figure 7 shows a simple example of a before-and-after punctuation and capitalization model.

Speech recognition industry impact

There are many unique applications for ASR . For example, speech recognition could help industries such as finance, telecommunications, and unified communications as a service (UCaaS) to improve customer experience, operational efficiency, and return on investment (ROI).

Speech recognition is applied in the finance industry for applications such as call center agent assist and trade floor transcripts. ASR is used to transcribe conversations between customers and call center agents or trade floor agents. The generated transcriptions can then be analyzed and used to provide real-time recommendations to agents. This adds to an 80% reduction in post-call time.

Furthermore, the generated transcripts are used for downstream tasks:

• Intent and entity recognition

Telecommunications

Contact centers are critical components of the telecommunications industry. With contact center technology, you can reimagine the telecommunications customer center, and speech recognition helps with that.

As previously discussed in the finance call center use case, ASR is used in Telecom contact centers to transcribe conversations between customers and contact center agents to analyze them and recommend call center agents in real time. T-Mobile uses ASR for quick customer resolution , for example.

Unified communications as a software

COVID-19 increased demand for UCaaS solutions, and vendors in the space began focusing on the use of speech AI technologies such as ASR to create more engaging meeting experiences.

For example, ASR can be used to generate live captions in video conferencing meetings. Captions generated can then be used for downstream tasks such as meeting summaries and identifying action items in notes.

Future of ASR technology

Speech recognition is not as easy as it sounds. Developing speech recognition is full of challenges, ranging from accuracy to customization for your use case to real-time performance. On the other hand, businesses and academic institutions are racing to overcome some of these challenges and advance the use of speech recognition capabilities.

ASR challenges

Some of the challenges in developing and deploying speech recognition pipelines in production include the following:

• Lack of tools and SDKs that offer state-of-the-art (SOTA) ASR models makes it difficult for developers to take advantage of the best speech recognition technology.
• Limited customization capabilities that enable developers to fine-tune on domain-specific and context-specific jargon, multiple languages, dialects, and accents to have your applications understand and speak like you
• Restricted deployment support; for example, depending on the use case, the software should be capable of being deployed in any cloud, on-premises, edge, and embedded. 
• Real-time speech recognition pipelines; for instance, in a call center agent assist use case, we cannot wait several seconds for conversations to be transcribed before using them to empower agents.

For more information about the major pain points that developers face when adding speech-to-text capabilities to applications, see Solving Automatic Speech Recognition Deployment Challenges .

ASR advancements

Numerous advancements in speech recognition are occurring on both the research and software development fronts. To begin, research has resulted in the development of several new cutting-edge ASR architectures, E2E speech recognition models, and self-supervised or unsupervised training techniques.

On the software side, there are a few tools that enable quick access to SOTA models, and then there are different sets of tools that enable the deployment of models as services in production. 

Key takeaways

Speech recognition continues to grow in adoption due to its advancements in deep learning-based algorithms that have made ASR as accurate as human recognition. Also, breakthroughs like multilingual ASR help companies make their apps available worldwide, and moving algorithms from cloud to on-device saves money, protects privacy, and speeds up inference.

NVIDIA offers Riva , a speech AI SDK, to address several of the challenges discussed above. With Riva, you can quickly access the latest SOTA research models tailored for production purposes. You can customize these models to your domain and use case, deploy on any cloud, on-premises, edge, or embedded, and run them in real-time for engaging natural interactions.

Learn how your organization can benefit from speech recognition skills with the free ebook, Building Speech AI Applications .

Related resources

• GTC session: Speech AI Demystified
• GTC session: Mastering Speech AI for Multilingual Multimedia Transformation
• GTC session: Human-Like AI Voices: Exploring the Evolution of Voice Technology
• NGC Containers: Domain Specific NeMo ASR Application
• NGC Containers: MATLAB
• Webinar: How Telcos Transform Customer Experiences with Conversational AI

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How Does Speech Recognition Work? (9 Simple Questions Answered)

• by Team Experts
• July 2, 2023 July 3, 2023

Discover the Surprising Science Behind Speech Recognition – Learn How It Works in 9 Simple Questions!

Speech recognition is the process of converting spoken words into written or machine-readable text. It is achieved through a combination of natural language processing , audio inputs, machine learning , and voice recognition . Speech recognition systems analyze speech patterns to identify phonemes , the basic units of sound in a language. Acoustic modeling is used to match the phonemes to words , and word prediction algorithms are used to determine the most likely words based on context analysis . Finally, the words are converted into text.

What is Natural Language Processing and How Does it Relate to Speech Recognition?

How do audio inputs enable speech recognition, what role does machine learning play in speech recognition, how does voice recognition work, what are the different types of speech patterns used for speech recognition, how is acoustic modeling used for accurate phoneme detection in speech recognition systems, what is word prediction and why is it important for effective speech recognition technology, how can context analysis improve accuracy of automatic speech recognition systems, common mistakes and misconceptions.

Natural language processing (NLP) is a branch of artificial intelligence that deals with the analysis and understanding of human language. It is used to enable machines to interpret and process natural language, such as speech, text, and other forms of communication . NLP is used in a variety of applications , including automated speech recognition , voice recognition technology , language models, text analysis , text-to-speech synthesis , natural language understanding , natural language generation, semantic analysis , syntactic analysis, pragmatic analysis, sentiment analysis, and speech-to-text conversion. NLP is closely related to speech recognition , as it is used to interpret and understand spoken language in order to convert it into text.

Audio inputs enable speech recognition by providing digital audio recordings of spoken words . These recordings are then analyzed to extract acoustic features of speech, such as pitch, frequency, and amplitude. Feature extraction techniques , such as spectral analysis of sound waves, are used to identify and classify phonemes . Natural language processing (NLP) and machine learning models are then used to interpret the audio recordings and recognize speech. Neural networks and deep learning architectures are used to further improve the accuracy of voice recognition . Finally, Automatic Speech Recognition (ASR) systems are used to convert the speech into text, and noise reduction techniques and voice biometrics are used to improve accuracy .

Machine learning plays a key role in speech recognition , as it is used to develop algorithms that can interpret and understand spoken language. Natural language processing , pattern recognition techniques , artificial intelligence , neural networks, acoustic modeling , language models, statistical methods , feature extraction , hidden Markov models (HMMs), deep learning architectures , voice recognition systems, speech synthesis , and automatic speech recognition (ASR) are all used to create machine learning models that can accurately interpret and understand spoken language. Natural language understanding is also used to further refine the accuracy of the machine learning models .

Voice recognition works by using machine learning algorithms to analyze the acoustic properties of a person’s voice. This includes using voice recognition software to identify phonemes , speaker identification, text normalization , language models, noise cancellation techniques , prosody analysis , contextual understanding , artificial neural networks, voice biometrics , speech synthesis , and deep learning . The data collected is then used to create a voice profile that can be used to identify the speaker .

The different types of speech patterns used for speech recognition include prosody , contextual speech recognition , speaker adaptation , language models, hidden Markov models (HMMs), neural networks, Gaussian mixture models (GMMs) , discrete wavelet transform (DWT), Mel-frequency cepstral coefficients (MFCCs), vector quantization (VQ), dynamic time warping (DTW), continuous density hidden Markov model (CDHMM), support vector machines (SVM), and deep learning .

Acoustic modeling is used for accurate phoneme detection in speech recognition systems by utilizing statistical models such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). Feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs) are used to extract relevant features from the audio signal . Context-dependent models are also used to improve accuracy . Discriminative training techniques such as maximum likelihood estimation and the Viterbi algorithm are used to train the models. In recent years, neural networks and deep learning algorithms have been used to improve accuracy , as well as natural language processing techniques .

Word prediction is a feature of natural language processing and artificial intelligence that uses machine learning algorithms to predict the next word or phrase a user is likely to type or say. It is used in automated speech recognition systems to improve the accuracy of the system by reducing the amount of user effort and time spent typing or speaking words. Word prediction also enhances the user experience by providing faster response times and increased efficiency in data entry tasks. Additionally, it reduces errors due to incorrect spelling or grammar, and improves the understanding of natural language by machines. By using word prediction, speech recognition technology can be more effective , providing improved accuracy and enhanced ability for machines to interpret human speech.

Context analysis can improve the accuracy of automatic speech recognition systems by utilizing language models, acoustic models, statistical methods , and machine learning algorithms to analyze the semantic , syntactic, and pragmatic aspects of speech. This analysis can include word – level , sentence- level , and discourse-level context, as well as utterance understanding and ambiguity resolution. By taking into account the context of the speech, the accuracy of the automatic speech recognition system can be improved.

• Misconception : Speech recognition requires a person to speak in a robotic , monotone voice. Correct Viewpoint: Speech recognition technology is designed to recognize natural speech patterns and does not require users to speak in any particular way.
• Misconception : Speech recognition can understand all languages equally well. Correct Viewpoint: Different speech recognition systems are designed for different languages and dialects, so the accuracy of the system will vary depending on which language it is programmed for.
• Misconception: Speech recognition only works with pre-programmed commands or phrases . Correct Viewpoint: Modern speech recognition systems are capable of understanding conversational language as well as specific commands or phrases that have been programmed into them by developers.

The SpeakWrite Blog

Ultimate guide to speech recognition technology (2023).

• April 12, 2023

Learn about speech recognition technology—how speech to text software works, benefits, limitations, transcriptions, and other real world applications.

Whether you’re a professional in need of more efficient transcription solutions or simply want your voice-enabled device to work smarter for you, this guide to speech recognition technology is here with all the answers.

Few technologies have evolved rapidly in recent years as speech recognition. In just the last decade, speech recognition has become something we rely on daily. From voice texting to Amazon Alexa understanding natural language queries, it’s hard to imagine life without speech recognition software.

But before deep learning was ever a word people knew, mid-century were engineers paving the path for today’s rapidly advancing world of automatic speech recognition. So let’s take a look at how speech recognition technologies evolved and speech-to-text became king.

What Is Speech Recognition Technology?

With machine intelligence and deep learning advances, speech recognition technology has become increasingly popular. Simply put, speech recognition technology (otherwise known as speech-to-text or automatic speech recognition) is software that can convert the sound waves of spoken human language into readable text. These programs match sounds to word sequences through a series of steps that include:

• Pre-processing: may consist of efforts to improve the audio of speech input by reducing and filtering the noise to reduce the error rate
• Feature extraction: this is the part where sound waves and acoustic signals are transformed into digital signals for processing using specialized speech technologies.
• Classification: extracted features are used to find spoken text; machine learning features can refine this process.
• Language modeling: considers important semantic and grammatical rules of a language while creating text.

How Does Speech Recognition Technology Work?

Speech recognition technology combines complex algorithms and language models to produce word output humans can understand. Features such as frequency, pitch, and loudness can then be used to recognize spoken words and phrases.

Here are some of the most common models for speech recognition, which include acoustic models and language models . Sometimes, several of these are interconnected and work together to create higher-quality speech recognition software and applications.

Natural Language Processing (NLP)

“Hey, Siri, how does speech-to-text work?”

Try it—you’ll likely hear your digital assistant read a sentence or two from a relevant article she finds online, all thanks to the magic of natural language processing.

Natural language processing is the artificial intelligence that gives machines like Siri the ability to understand and answer human questions. These AI systems enable devices to understand what humans are saying, including everything from intent to parts of speech.

But NLP is used by more than just digital assistants like Siri or Alexa—it’s how your inbox knows which spam messages to filter, how search engines know which websites to offer in response to a query, and how your phone knows which words to autocomplete.

Neural Networks

Neural networks are one of the most powerful AI applications in speech recognition. They’re used to recognize patterns and process large amounts of data quickly.

For example, neural networks can learn from past input to better understand what words or phrases you might use in a conversation. It uses those patterns to more accurately detect the words you’re saying.

Leveraging cutting-edge deep learning algorithms, neural networks are revolutionizing how machines recognize speech commands. By imitating neurons in our brains and creating intricate webs of electrochemical connections between them, these robust architectures can process data with unparalleled accuracy for various applications such as automatic speech recognition.

Hidden Markov Models (HMM)

The Hidden Markov Model is a powerful tool for acoustic modeling, providing strong analytical capabilities to accurately detect natural speech. Its application in the field of Natural Language Processing has allowed researchers to efficiently train machines on word generation tasks, acoustics, and syntax to create unified probabilistic models.

Speaker Diarization

Speaker diarization is an innovative process that segments audio streams into distinguishable speakers, allowing the automatic speech recognition transcript to organize each speaker’s contributions separately. Using unique sound qualities and word patterns, this technique pinpoints conversations accurately so every voice can be heard.

The History of Speech Recognition Technology

It’s hard to believe that just a few short decades ago, the idea of having a computer respond to speech felt like something straight out of science fiction. Yet, Fast-forward to today, and voice-recognition technology has gone from being an obscure concept to becoming so commonplace you can find it in our smartphones.

But where did this all start? First, let’s take a look at the history of speech recognition technology – from its uncertain early days through its evolution into today’s easy-to-use technology.

Speech recognition technology has existed since the 1950s when Bell Laboratory researchers first developed systems to recognize simple commands . However, early speech recognition systems were limited in their capabilities and could not identify more complex phrases or sentences.

In the 1980s, advances in computing power enabled the development of better speech recognition systems that could understand entire sentences. Today, speech recognition technology has become much more advanced, with some systems able to recognize multiple languages and dialects with high accuracy.

Timeline of Speech Recognition Programs

• 1952 – Bell Labs researchers created “Audrey,” an innovative system for recognizing individual digits. Early speech recognition systems were limited in their capabilities and could not identify more complex phrases or sentences.
• 1962 – IBM shook the tech sphere in 1962 at The World’s Fair, showcasing a remarkable 16-word speech recognition capability – nicknamed “Shoebox” —that left onlookers awestruck.
• 1980s – IBM revolutionized the typewriting industry in the 1980s with Tangora , a voice-activated system that could understand up to 20,000 words. Advances in computing power enabled the development of better speech recognition systems that could understand entire sentences.
• 1996 – IBM’s VoiceType Simply Speaking application recognized 42,000 English and Spanish words.
• 2007 – Google launched GOOG-411 as a telephone directory service, an endeavor that provided immense amounts of data for improving speech recognition systems over time. Now, this technology is available across 30 languages through Google Voice Search .
• 2017 – Microsoft made history when its research team achieved the remarkable goal of transcribing phone conversations utilizing various deep-learning models.

How is Speech Recognition Used Today?

Speech recognition technology has come a long way since its inception at Bell Laboratories.

Today, speech recognition technology has become much more advanced, with some systems able to recognize multiple languages and dialects with high accuracy and low error rates.

Speech recognition technology is used in a wide range of applications in our daily lives, including:

• Voice Texting: Voice texting is a popular feature on many smartphones that allow users to compose text messages without typing.
• Smart Home Automation: Smart home systems use voice commands technology to control lights, thermostats, and other household appliances with simple commands.
• Voice Search: Voice search is one of the most popular applications of speech recognition, as it allows users to quickly
• Transcription: Speech recognition technology can transcribe spoken words into text fast.
• Military and Civilian Vehicle Systems: Speech recognition technology can be used to control unmanned aerial vehicles, military drones, and other autonomous vehicles.
• Medical Documentation: Speech recognition technology is used to quickly and accurately transcribe medical notes, making it easier for doctors to document patient visits.

Key Features of Advanced Speech Recognition Programs

If you’re looking for speech recognition technology with exceptional accuracy that can do more than transcribe phonetic sounds, be sure it includes these features.

Acoustic training

Advanced speech recognition programs use acoustic training models to detect natural language patterns and better understand the speaker’s intent. In addition, acoustic training can teach AI systems to tune out ambient noise, such as the background noise of other voices.

Speaker labeling

Speaker labeling is a feature that allows speech recognition systems to differentiate between multiple speakers, even if they are speaking in the same language. This technology can help keep track of who said what during meetings and conferences, eliminating the need for manual transcription.

Dictionary customization

Advanced speech recognition programs allow users to customize their own dictionaries and include specialized terminology to improve accuracy. This can be especially useful for medical professionals who need accurate documentation of patient visits.

If you don’t want your transcript to include any naughty words, then you’ll want to make sure your speech recognition system consists of a filtering feature. Filtering allows users to specify which words should be filtered out of their transcripts, ensuring that they are clean and professional.

Language weighting

Language weighting is a feature used by advanced speech recognition systems to prioritize certain commonly used words over others. For example, this feature can be helpful when there are two similar words, such as “form” and “from,” so the system knows which one is being spoken.

The Benefits of Speech Recognition Technology

Human speech recognition technology has revolutionized how people navigate, purchase, and communicate. Additionally, speech-to-text technology provides a vital bridge to communication for individuals with sight and auditory disabilities. Innovations like screen readers, text-to-speech dictation systems, and audio transcriptions help make the world more accessible to those who need it most.

Limits of Speech Recognition Programs

Despite its advantages, speech recognition technology still needs to be improved.

• Accuracy rate and reliability – the quality of the audio signal and the complexity of the language being spoken can significantly impact the system’s ability to accurately interpret spoken words. For now, speech-to-text technology has a higher average error rate than humans.
• Formatting – Exporting speech recognition results into a readable format, such as Word or Excel, can be difficult and time-consuming—especially if you must adhere to professional formatting standards.
• Ambient noise – Speech recognition systems are still incapable of reliably recognizing speech in noisy environments. If you plan on recording yourself and turning it into a transcript later, make sure the environment is quiet and free from distractions.
• Translation – Human speech and language are difficult to translate word for word, as things like syntax, context, and cultural differences can lead to subtle meanings that are lost in direct speech-to-text translations.
• Security – While speech recognition systems are great for controlling devices, you don’t always have control over how your data is stored and used once recorded.

Using Speech Recognition for Transcriptions

Speech recognition technology is commonly used to transcribe audio recordings into text documents and has become a standard tool in business and law enforcement. There are handy apps like Otter.ai that can help you quickly and accurately transcribe and summarize meetings and speech-to-text features embedded in document processors like Word.

However, you should use speech recognition technology for transcriptions with caution because there are a number of limitations that could lead to costly mistakes.

If you’re creating an important legal document or professional transcription , relying on speech recognition technology or any artificial intelligence to provide accurate results is not recommended. Instead, it’s best to employ a professional transcription service or hire an experienced typist to accurately transcribe audio recordings.

Human typists have an accuracy level of 99% – 100%, can follow dictation instructions, and can format your transcript appropriately depending on your instructions. As a result, there is no need for additional editing once your document is delivered (usually in 3 hours or less), and you can put your document to use immediately.

Unfortunately, speech recognition technology can’t achieve these things yet. You can expect an accuracy of up to 80% and little to no professional formatting. Additionally, your dictation instructions will fall on deaf “ears.” Frustratingly, they’ll just be included in the transcription rather than followed to a T. You’ll wind up spending extra time editing your transcript for readability, accuracy, and professionalism.

So if you’re looking for dependable, accurate, fast transcriptions, consider human transcription services instead.

Is Speech Recognition Technology Accurate?

The accuracy of speech recognition technology depends on several factors, including the quality of the audio signal, the complexity of the language being spoken, and the specific algorithms used by the system.

Some speech recognition software can withstand poor acoustic quality, identify multiple speakers, understand accents, and even learn industry jargon. Others are more rudimentary and may have limited vocabulary or may only be able to work with pristine audio quality.

Speaker identification vs. speech recognition: what’s the difference?

The two are often used interchangeably. However, there is a distinction. Speech recognition technology shouldn’t be confused with speech identification technology, which identifies who is speaking rather than what the speaker has to say.

What type of technology is speech recognition?

Speech recognition is a type of technology that allows computers to understand and interpret spoken words. It is a form of artificial intelligence (AI) that uses algorithms to recognize patterns in audio signals, such as the sound of speech. Speech recognition technology has been around for decades.

Is speech recognition AI technology?

Yes, speech recognition is a form of artificial intelligence (AI) that uses algorithms to recognize patterns in audio signals, such as the sound of speech. Speech recognition technology has been around for decades, but it wasn’t until recently that systems became sophisticated enough to accurately understand and interpret spoken words.

What are examples of speech recognition devices?

Examples of speech recognition devices include virtual assistants such as Amazon Alexa, Google Assistant, and Apple Siri. Additionally, many mobile phones and computers now come with built-in voice recognition software that can be used to control the device or issue commands. Speech recognition technology is also used in various other applications, such as automated customer service systems, medical transcription software, and real-time language translation systems.

See How Much Your Business Could Be Saving in Transcription Costs

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From Talk to Tech: Exploring the World of Speech Recognition

What is Speech Recognition Technology?

Imagine being able to control electronic devices, order groceries, or dictate messages with just voice. Speech recognition technology has ushered in a new era of interaction with devices, transforming the way we communicate with them. It allows machines to understand and interpret human speech, enabling a range of applications that were once thought impossible.

Speech recognition leverages machine learning algorithms to recognize speech patterns, convert audio files into text, and examine word meaning. Siri, Alexa, Google's Assistant, and Microsoft's Cortana are some of the most popular speech to text voice assistants used today that can interpret human speech and respond in a synthesized voice.

From personal assistants that can understand every command directed towards them to self-driving cars that can comprehend voice instructions and take the necessary actions, the potential applications of speech recognition are manifold. As technology continues to advance, the possibilities are endless.

How do Speech Recognition Systems Work?

speech to text processing is traditionally carried out in the following way:

Recording the audio:  The first step of speech to text conversion involves recording the audio and voice signals using a microphone or other audio input devices.

Breaking the audio into parts: The recorded voice or audio signals are then broken down into small segments, and features are extracted from each piece, such as the sound's frequency, pitch, and duration.

Digitizing speech into computer-readable format:  In the third step, the speech data is digitized into a computer-readable format that identifies the sequence of characters to remember the words or phrases that were most likely spoken.

Decoding speech using the algorithm:  Finally, language models decode the speech using speech recognition algorithms to produce a transcript or other output.

To adapt to the nature of human speech and language, speech recognition is designed to identify patterns, speaking styles, frequency of words spoken, and speech dialects on various levels. Advanced speech recognition software are also capable of eliminating background noises that often accompany speech signals.

When it comes to processing human speech, the following two types of models are used:

Acoustic Models

Acoustic models are a type of machine learning model used in speech recognition systems. These models are designed to help a computer understand and interpret spoken language by analyzing the sound waves produced by a person's voice.

Language Models

Based on the speech context, language models employ statistical algorithms to forecast the likelihood of words and phrases. They compare the acoustic model's output to a pre-built vocabulary of words and phrases to identify the most likely word order that makes sense in a given context of the speech. 

Applications of Speech Recognition Technology

Automatic speech recognition is becoming increasingly integrated into our daily lives, and its potential applications are continually expanding. With the help of speech to text applications, it's now becoming convenient to convert a speech or spoken word into a text format, in minutes.

Speech recognition is also used across industries, including healthcare , customer service, education, automotive, finance, and more, to save time and work efficiently. Here are some common speech recognition applications:

Voice Command for Smart Devices

Today, there are many home devices designed with voice recognition. Mobile devices and home assistants like Amazon Echo or Google Home are among the most widely used speech recognition system. One can easily use such devices to set reminders, place calls, play music, or turn on lights with simple voice commands.

Online Voice Search

Finding information online is now more straightforward and practical, thanks to speech to text technology. With online voice search, users can search using their voice rather than typing. This is an excellent advantage for people with disabilities and physical impairments and those that are multitasking and don't have the time to type a prompt.

Help People with Disabilities

People with disabilities can also benefit from speech to text applications because it allows them to use voice recognition to operate equipment, communicate, and carry out daily duties. In other words, it improves their accessibility. For example, in case of emergencies, people with visual impairment can use voice commands to call their friends and family on their mobile devices.

Business Applications of Speech Recognition

Speech recognition has various uses in business, including banking, healthcare, and customer support. In these industries, voice recognition mainly aims at enhancing productivity, communication, and accessibility. Some common applications of speech technology in business sectors include:

Speech recognition is used in the banking industry to enhance customer service and expedite internal procedures. Banks can also utilize speech to text programs to enable clients to access their accounts and conduct transactions using only their voice.

Customers in the bank who have difficulties entering or navigating through complicated data will find speech to text particularly useful. They can simply voice search the necessary data. In fact, today, banks are automating procedures like fraud detection and customer identification using this impressive technology, which can save costs and boost security.

Voice recognition is used in the healthcare industry to enhance patient care and expedite administrative procedures. For instance, physicians can dictate notes about patient visits using speech recognition programs, which can then be converted into electronic medical records. This also helps to save a lot of time, and correct data is recorded in the best way possible with this technology.

Customer Support

Speech recognition is employed in customer care to enhance the customer experience and cut expenses. For instance, businesses can automate time-consuming processes using speech to text so that customers can access information and solve problems without speaking to a live representative. This could shorten wait times and increase customer satisfaction.

Challenges with Speech Recognition Technology

Although speech recognition has become popular in recent years and made our lives easier, there are still several challenges concerning speech recognition that needs to be addressed.

Accuracy may not always be perfect

A speech recognition software can still have difficulty accurately recognizing speech in noisy or crowded environments or when the speaker has an accent or speech impediment. This can lead to incorrect transcriptions and miscommunications.

The software can not always understand complexity and jargon

Any speech recognition software has a limited vocabulary, so it may struggle to identify uncommon or specialized vocabulary like complex sentences or technical jargon, making it less useful in specific industries or contexts. Errors in interpretation or translation may happen if the speech recognition fails to recognize the context of words or phrases.

Concern about data privacy, data can be recorded.

Speech recognition technology relies on recording and storing audio data, which can raise concerns about data privacy. Users may be uncomfortable with their voice recordings being stored and used for other purposes. Also, voice notes, phone calls, and recordings may be recorded without the user's knowledge, and hacking or impersonation can be vulnerable to these security breaches. These things raise privacy and security concerns.

Software that Use Speech Recognition Technology

Many software programs use speech recognition technology to transcribe spoken words into text. Here are some of the most popular ones:

Nuance Dragon.

Amazon Transcribe.

Google Text to Speech

Watson Speech to Text

To sum up, speech recognition technology has come a long way in recent years. Given its benefits, including increased efficiency, productivity, and accessibility, its finding applications across a wide range of industries. As we continue to explore the potential of this evolving technology, we can expect to see even more exciting applications emerge in the future.

With the power of AI and machine learning at our fingertips, we're poised to transform the way we interact with technology in ways we never thought possible. So, let's embrace this exciting future and see where speech recognition takes us next!

What are the three steps of speech recognition?

The three steps of speech recognition are as follows:

Step 1: Capture the acoustic signal

The first step is to capture the acoustic signal using an audio input device and later pre-process the motion to remove noise and other unwanted sounds. The movement is then broken down into small segments, and features such as frequency, pitch, and duration are extracted from each piece.

Step 2: Combining the acoustic and language models

The second step involves combining the acoustic and language models to produce a transcription of the spoken words and word sequences.

Step 3: Converting the text into a synthesized voice

The final step is converting the text into a synthesized voice or using the transcription to perform other actions, such as controlling a computer or navigating a system.

What are examples of speech recognition?

Speech recognition is used in a wide range of applications. The most famous examples of speech recognition are voice assistants like Apple's Siri, Amazon's Alexa, and Google Assistant. These assistants use effective speech recognition to understand and respond to voice commands, allowing users to ask questions, set reminders, and control their smart home devices using only voice.

What is the importance of speech recognition?

Speech recognition is essential for improving accessibility for people with disabilities, including those with visual or motor impairments. It can also improve productivity in various settings and promote language learning and communication in multicultural environments. Speech recognition can break down language barriers, save time, and reduce errors.

You should also read:

Understanding Speech to Text in Depth

Top 10 Speech to Text Software in 2024

How Speech Recognition is Changing Language Learning

What is ASR? How Does it Work? Our In-Depth 2023 Guide

Ever wondered how Siri or Alexa magically transcribes your voice commands into text? Well, it's all thanks to speech-to-text algorithms and ASR systems that help make our lives easier! From contact centers to healthcare, ASR technology is transforming various industries with its multitude of use cases.

In this comprehensive guide, we'll explore the ins and outs of speech recognition systems powered by state-of-the-art machine learning and deep learning techniques. You'll learn about the role of end-to-end transformer models, neural networks, and natural language processing (NLP) in decoding spoken language. We'll also touch upon the importance of datasets, acoustic models, and ASR models in training these language models.

We'll also chat about real-time applications, APIs, and interfaces that make ASR technology accessible to everyone, from Amazon's Alexa and Apple's Siri to Microsoft's voice recognition software. Plus, you'll get a glimpse of the nitty-gritty details like n-grams, phonemes, and waveforms that help make ASR systems more accurate.

What's more, we'll look at benchmarks, word error rate (WER) optimization, and how providers handle challenges like background noise, insertions, and variants in speech data. And if you're hungry for more, we'll point you to some great tutorials and resources to learn about related technologies like text-to-speech synthesis, sentiment analysis, and much more!

So, buckle up and get ready for a thrilling journey through the world of automatic speech recognition technology!

What is Automatic Speech Recognition(ASR)?

Automatic Speech Recognition (ASR) is a fascinating subfield of artificial intelligence that focuses on converting spoken language into written text. It's a technology that's been evolving for decades and has become an integral part of our daily lives, powering everything from voice assistants like Siri and Alexa to transcription services and customer support systems in contact centers.

At the core of ASR technology lies the complex interplay of algorithms, neural networks, and machine learning models. These elements work together to decode and transcribe speech data accurately and efficiently. The goal of ASR is to mimic the human ability to understand spoken language, making it easier for us to interact with devices, services, and applications using our natural way of communication - speech.

The process of ASR involves several essential components, such as acoustic modeling, language modeling, and decoding. Acoustic modeling is concerned with the relationship between the spoken language's phonemes (basic units of sound) and the audio waveform captured by a microphone. These models are trained on vast datasets containing a variety of speech samples to recognize different accents, dialects, and pronunciations. This training helps the ASR system become more versatile and able to handle variations in speech patterns.

Language modeling, on the other hand, deals with understanding the structure and grammar of the spoken language. Techniques like n-grams and more advanced neural network-based approaches, such as transformers, are employed to predict word sequences and capture the contextual information of speech. This component enables ASR systems to better distinguish between homophones and correct word sequences based on the surrounding context.

The decoding stage is where the magic happens – combining the outputs of acoustic and language models to generate the most probable transcription of the spoken language. This stage often requires optimization to minimize the word error rate (WER) and other performance metrics, making the ASR system more accurate and reliable.

In recent years, deep learning and end-to-end approaches have made significant strides in improving the performance of ASR systems. These models simplify the traditional ASR pipeline by directly learning the mapping between speech waveforms and text. As a result, they can achieve state-of-the-art performance and offer real-time capabilities for various applications, from voice assistants and transcription services to healthcare and customer support systems.

As ASR technology continues to advance, we can expect even more seamless integration of speech recognition into our lives, making it easier for us to interact with technology and opening up new possibilities for communication and accessibility.

What is NLP and Why is It Used in Speech Recognition?

Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and humans through natural language. In essence, NLP aims to enable machines to understand, interpret, and generate human language in a manner that is both meaningful and contextually relevant. This involves tackling various challenges such as syntax, semantics, and pragmatics that make human language complex and nuanced.

NLP plays a crucial role in speech recognition, as it helps bridge the gap between the raw acoustic signals captured by ASR systems and the rich, meaningful structure of human language. By applying NLP techniques to the output generated by ASR, we can extract valuable insights, detect patterns, and improve the overall quality of the transcriptions.

One of the key reasons NLP is used in speech recognition is its ability to understand context. In spoken language, words and phrases can have multiple meanings, depending on the surrounding words and the speaker's intent. NLP techniques, such as context-aware language modeling and semantic analysis, enable ASR systems to disambiguate homophones and generate more accurate transcriptions based on the meaning derived from the broader context.

Another important aspect of NLP in speech recognition is its capacity to handle variations in human language. Spoken language is inherently diverse, with regional accents, dialects, slang, and colloquial expressions adding layers of complexity. NLP helps ASR systems to better handle this diversity by incorporating linguistic knowledge and advanced machine learning models, making them more robust and adaptable to different speech patterns.

Additionally, NLP can be used to enhance the user experience in speech recognition applications. For example, sentiment analysis can be employed to gauge a speaker's emotions or attitudes, enabling applications like customer support systems to provide more empathetic and tailored responses. Meanwhile, text summarization can be used to condense long transcriptions into concise summaries, making it easier for users to review and digest the content.

How Does Automatic Speech Recognition Work?

At its core, Automatic Speech Recognition (ASR) technology aims to convert spoken language into written text by processing and interpreting the complex patterns of human speech. While the intricacies of ASR systems can be quite technical, here's a not-too-technical overview of the mechanism behind this fascinating technology.

• Audio capture: The ASR process begins when a microphone or another input device captures the speaker's voice as an audio waveform. This continuous signal represents the various sound frequencies and amplitudes present in the speech.
• Feature extraction: The raw audio waveform is then processed to extract relevant features, such as pitch, intensity, and spectral characteristics. These features help the ASR system identify and differentiate between various phonemes, which are the basic units of sound in any spoken language.
• Acoustic modeling: Acoustic models are trained on large datasets containing numerous speech samples to recognize the relationship between the extracted features and the corresponding phonemes. These models can be based on traditional techniques like Hidden Markov Models or more advanced deep learning methods like neural networks.
• Language modeling: While acoustic models deal with the sounds of speech, language models focus on understanding the structure, grammar, and context of the language. These models estimate the probability of a sequence of words occurring together, helping the ASR system to generate more accurate transcriptions by considering the context of the spoken language. N-grams and neural network-based approaches like transformers are commonly used in language modeling.
• Decoding: The decoding stage combines the outputs of the acoustic and language models to produce the most probable transcription of the spoken language. Various algorithms and techniques, such as beam search and dynamic time warping, are employed to align the acoustic and language model outputs and generate the final transcription.
• Post-processing: Once the transcription is generated, additional Natural Language Processing (NLP) techniques can be applied to refine the output. This can include tasks such as spell-checking, grammar correction, or sentiment analysis to provide a more polished and meaningful transcription.

In recent years, end-to-end approaches have simplified the traditional ASR pipeline by directly learning the mapping between speech waveforms and text using deep learning models. These models, such as the ones based on the transformer architecture, have shown significant improvements in performance, leading to more accurate and real-time speech recognition capabilities.

Speech Recognition Algorithms

As ASR technology has evolved over the years, different speech recognition algorithms have been developed to improve accuracy and adaptability. Each of these algorithms approaches the problem of speech recognition from a different perspective, leveraging various techniques and methodologies. In this section, we'll explore some of the most notable speech recognition algorithms that have made a significant impact on the field.

Hidden Markov Models (HMMs)

Hidden Markov Models (HMMs) have been the cornerstone of traditional speech recognition systems for several decades. HMMs are statistical models that represent the probabilistic relationships between observed sequences of features (such as phonemes) and the underlying hidden states. In the context of ASR, HMMs are used to model the time-varying nature of speech signals and make predictions based on the observed acoustic features. HMM-based systems usually require separate components for acoustic and language modeling, with the decoding step combining these models to generate the final transcription.

Deep Learning-Based Models

With the advent of deep learning, several neural network architectures have been employed in ASR systems to improve their performance. Some of the most notable deep learning-based models include:

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a type of neural network designed to process sequential data, making them well-suited for speech recognition tasks. RNNs have memory cells that allow them to maintain a hidden state, capturing information from previous time steps. This ability to model temporal dependencies makes RNNs particularly effective in modeling the dynamic nature of speech signals. Variants of RNNs, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), have been developed to address the vanishing gradient problem and enable more efficient learning of long-term dependencies.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are primarily known for their success in image recognition tasks, but they have also proven useful in speech recognition. CNNs can process local patterns in the input data through a series of convolutional layers, making them capable of capturing spatial and temporal features in speech signals. In ASR systems, CNNs are often combined with other types of neural networks, such as RNNs or LSTMs, to capture both the local and global context of the speech data.

Transformer Models

Transformer models have gained significant attention in recent years due to their success in natural language processing tasks. These models rely on self-attention mechanisms to process input data in parallel, rather than sequentially, which allows them to capture long-range dependencies more effectively than traditional RNNs. In the context of ASR, end-to-end transformer models have been used to map speech waveforms directly to text, simplifying the speech recognition pipeline and achieving state-of-the-art performance.

Each of these algorithms has contributed to the advancement of ASR technology in different ways, pushing the boundaries of what is possible in terms of accuracy, speed, and adaptability. As research continues to explore new techniques and approaches, we can expect even more innovative algorithms to emerge, further enhancing the capabilities of speech recognition systems.

Applications of ASR

Automatic Speech Recognition (ASR) technology has found its way into numerous applications across various industries, thanks to its ability to transcribe and process spoken language effectively. These applications range from enhancing user experiences in consumer devices to improving productivity and accessibility in professional settings. In this section, we'll explore some of the most prominent applications of ASR technology, showcasing its versatility and transformative potential.

Voice Assistants

One of the most well-known applications of ASR technology is voice assistants, such as Amazon's Alexa, Apple's Siri, and Google Assistant. These AI-powered virtual assistants rely on ASR systems to understand and respond to voice commands, making it possible for users to interact with their devices using natural speech. Voice assistants have become ubiquitous in smartphones, smart speakers, and other consumer electronics, facilitating hands-free control, information retrieval, and various other tasks.

Transcription Services

ASR technology has revolutionized transcription services by automating the process of converting spoken language into written text. This automation has significantly reduced the time and effort required for transcription, enabling faster turnaround times and cost savings. Applications include transcription of meetings, interviews, lectures, podcasts, and even real-time captioning for live events, enhancing accessibility for individuals with hearing impairments.

Customer Support and Contact Centers

Contact centers and customer support services have embraced ASR technology to streamline their operations and provide better experiences for both customers and agents. ASR systems can transcribe customer calls, enabling real-time sentiment analysis, keyword detection, and call summarization. This information can be used to route calls to the appropriate agent, monitor agent performance, and identify areas for improvement. Additionally, ASR technology can be used in Interactive Voice Response (IVR) systems, allowing customers to navigate through automated menus using spoken commands.

The healthcare industry has also benefited from the advancements in ASR technology. Medical professionals can use ASR systems to dictate patient notes, diagnostic reports, and other documentation, saving time and improving the accuracy of medical records. In addition, speech recognition technology can be used in telemedicine applications, enabling real-time transcription and remote communication between healthcare providers and patients.

Language Learning and Accessibility

ASR technology has proven valuable in language learning applications, providing real-time feedback on pronunciation and fluency. By transcribing and analyzing spoken language, ASR systems can identify areas for improvement and provide personalized guidance to learners. Additionally, ASR technology can enhance accessibility for individuals with speech or hearing impairments, enabling them to interact with devices and services more easily through speech-to-text and text-to-speech conversion.

These applications represent just a glimpse of the diverse use cases for ASR technology. As speech recognition systems continue to advance, we can expect even more innovative applications to emerge, further transforming the way we communicate and interact with technology.

Future of ASR: Challenges and Opportunities

The future of ASR technology is promising, with ongoing advancements in artificial intelligence, machine learning, and natural language processing opening up new possibilities and applications. However, along with these exciting opportunities come several challenges that need to be addressed to fully unlock the potential of ASR systems. In this section, we'll discuss both the opportunities and challenges that lie ahead for ASR technology.

Opportunities

Multilingual and multidialectal asr.

One significant opportunity for ASR technology is the development of systems that can effectively handle multiple languages and dialects. As the world becomes more interconnected, the demand for speech recognition systems that can understand and transcribe various languages and dialects will continue to grow. Advancements in machine learning and deep learning techniques can help ASR systems become more adaptable and versatile, catering to the diverse linguistic needs of the global population.

Improved Robustness in Noisy Environments

Another area of opportunity is improving the robustness of ASR systems in noisy environments. Background noise, overlapping speech, and other acoustic challenges can significantly impact the performance of speech recognition systems. Developing algorithms and techniques to better handle these challenges will enable more accurate and reliable ASR in real-world scenarios, expanding its applicability across various industries and use cases.

Real-time and Low-latency ASR

As the demand for real-time applications grows, so does the need for low-latency ASR systems. Advancements in both hardware and software can help reduce the processing time required for speech recognition, allowing for more seamless and responsive user experiences. This could be particularly beneficial in domains such as real-time transcription, live event captioning, and voice-controlled applications.

Privacy and Security

One of the primary challenges facing ASR technology is ensuring privacy and security. With the increasing prevalence of voice assistants and other speech-enabled devices, concerns about the collection, storage, and use of voice data have grown. Developing methods to protect user privacy while maintaining the effectiveness of ASR systems will be crucial for gaining user trust and ensuring the responsible use of speech recognition technology.

Addressing Bias and Fairness

ASR systems are trained on large datasets, which may contain biases that can be inadvertently learned by the models. Addressing issues of bias and fairness in ASR technology is essential to ensure that speech recognition systems work equally well for all users, regardless of their accents, dialects, or speech patterns. This requires the collection of more diverse and representative training data, as well as the development of algorithms and techniques that actively mitigate bias.

Computational Efficiency

Deep learning-based ASR models, while highly effective, can be computationally expensive, especially for end-to-end systems. Reducing the computational requirements of ASR models without sacrificing performance is a challenge that needs to be addressed to make speech recognition technology more accessible and energy-efficient, particularly for edge devices and low-resource environments.

Key Takeaways

In a nutshell, Automatic Speech Recognition (ASR) technology has come a long way, transforming how we interact with devices and services through various algorithms and techniques. From traditional methods like HMMs to cutting-edge deep learning models, ASR systems have evolved to become more accurate and adaptable. Today, ASR technology finds applications in voice assistants, transcription services, customer support, healthcare, language learning, and accessibility, among others.

As we look to the future, the potential for ASR technology is immense, with opportunities for multilingual support, improved robustness, and real-time processing. However, challenges related to privacy, security, bias, fairness, and computational efficiency must be addressed to fully unlock this potential.

If you're interested in experiencing the power of cutting-edge ASR technology for yourself, check out Simon Says AI . Simon Says offers a user-friendly platform for accurate and efficient transcription, making it an invaluable tool for content creators, professionals, and organizations alike. Give it a try and see how ASR technology can transform your workflow today!

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Speech Recognition AI: What is it and How Does it Work|Gnani

A Beginner’s Guide to Speech Recognition AI

AI speech recognition is a technology that allows computers and applications to understand human speech data . It is a feature that has been around for decades, but it has increased in accuracy and sophistication in recent years.

Speech recognition works by using artificial intelligence to  recognize the words or language that a person speaks and then translate that content into text. It’s important to note that this technology is still in its infancy but is improving its accuracy rapidly.

What is Speech Recognition AI?

Speech recognition enables computers , applications and software to comprehend and translate human speech data into text  for business solutions . The speech recognition model works by using artificial intelligence (AI) to analyze your voice and language , identify by learning the words you are saying, and then output those words with transcription accuracy as model content or text data on a screen.

Speech Recognition in AI

Speech recognition is a significant part of artificial intelligence (AI) applications . AI is a machine’s ability to mimic human behaviour by learning from its environment. Speech recognition enables computers and software applications to “understand” what people are saying, which allows them to process information faster and with high accuracy. Speech recognition is also used as models in voice assistants like Siri and Alexa, which allow users to interact with computers using natural transcription language data or content .

Thanks to recent advancements, speech recognition technology is now more precise and widely used than in the past. It is used in various fields, including healthcare, customer service, education, and entertainment. However, there are still challenges to overcome, such as better handling of accents and dialects and the difficulty of recognizing speech in noisy environments. Despite these challenges, speech recognition is an exciting area of artificial intelligence with great potential for future development.

How Does Speech Recognition AI Work?

Speech recognition or voice recognition is a complex process that involves audio accuracy over several steps and data or language solutions , including:

• Recognizing the words , models and content in the user’s speech or audio . This business accuracy step requires training the model to identify each word in your vocabulary or audio cloud .
• Converting those audios and language into text. This step involves converting recognized audios i nto letters or numbers (called phonemes) so that other parts of the AI software solutions system can process th ose models .
• Determining what was said. Next, AI looks at which content and words were spoken most often and how frequently they were used together to determine their meaning (this process is known as “predictive modelling”).
• Parsing out commands from the rest of your speech or audio content (also known as disambiguation).

Speech Recognition AI and Natural Language Processing

Natural Language Processing is a part of artificial intelligence that involves analyzing data related to natural language and converting it into a machine- comprehendible format. Speech recognition and AI play a pivotal role in NLPs in improving the accuracy and efficiency of human language recognition. 

A lot of businesses now include speech-to-text software or speech recognition AI to enhance their business applications and improve customer experience. By using speech recognition AI and natural language processing together, companies can transcribe calls, meetings etc. Giant companies like Apple, Google, and Amazon are leveraging AI-based speech or voice recognition applications to provide a flawless customer experience. 

Use Cases of Speech Recognition AI

Speech recognition AI is being used as business solutions in many industries and applications . From ATMs to call centers and voice-activated audio content assistants, AI is helping people interact with technology and software more naturally with better data transcription accuracy than ever before.

Call Centers

Speech recognition is one of the most popular uses of speech AI in call centers. This technology allows you to listen to what customers are saying and then use that information via cloud models to respond appropriately.

You can also use speech recognition technology for voice or audio biometrics, which means using voice patterns as proof of identity or authorization for access solutions or services without relying on passwords or other traditional methods or models like fingerprints or eye scans. This can eliminate business issues like forgotten passwords or compromised security codes in favor of something more secure: your voice!

Banking and financial institutions are using speech AI applications to help customers with their business queries. For example, you can ask a bank about your account balance or the current interest rate on your savings account. This cuts down on the time it takes for customer service representatives to answer questions they would typically have to research and look at cloud data , which means quicker response times and better customer service.

Telecommunications

Speech-enabled AI is a technology that’s gaining traction in the telecommunications industry. Speech recognition technology models enable calls to be analyzed and managed more efficiently. This allows agents to focus on their highest-value tasks to deliver better customer service.

Customers can now interact with businesses in real-time 24/7 via voice transcription solutions or text messaging applications , which makes them feel more connected with the company and improves their overall experience.

Speech AI is a learning technology used in many different areas as transcription solutions . Healthcare is one of the most important, as it can help doctors and nurses care for their patients better. Voice-activated devices use learning models that allow patients to communicate with doctors, nurses, and other healthcare professionals without using their hands or typing on a keyboard.

Doctors can use speech recognition AI via cloud data to help patients understand their feelings and why they feel that way. It’s much easier than having them read through a brochure or pamphlet—and it’s more engaging. Speech AI can also take down patient histories and help with medical transcriptions.

Media and Marketing

Tools such as dictation software use speech recognition and AI to help users type or write more in much less time. Roughly speaking, copywriters and content writers can transcribe as much as 3000-4000 words in as less as half an hour on an average.

Accuracy, though, is a factor. These tools don’t guarantee 100% foolproof transcription. Still, they are extremely beneficial in helping media and marketing people in composing their first drafts.

Challenges in Working with Speech Recognition AI

There are many challenges in working with speech AI. For example, both technology and cloud are new and developing rapidly. As a result, it isn’t easy to make accurate predictions about how long it will take for a company to build its speech-enabled product.

Another challenge with speech AI is getting the right tools to analyze your data. Most people need access to this technology or cloud , so finding the right tool for your requirements may take time and effort.

You must use the correct language and syntax when creating your algorithms on cloud . This can be difficult because it requires understanding how computers and humans communicate. Speech recognition still needs improvement, and it can be difficult for computers to understand every word you say.

If you use speech recognition software, you will need to train it on your voice before it can understand what you’re saying. This can take a long time and requires careful study of how your voice sounds different from other people’s.

The other concern is that there are privacy laws surrounding medical records. These laws vary from state to state, so you’ll need to check with your jurisdiction before implementing speech AI technology.

Educating your staff on the technology and how it works is important if you decide to use speech AI. This will help them understand what they’re recording and why they’re recording it.

Frequently Asked Questions

How does speech recognition work.

Speech recognition AI is the process of converting spoken language into text. The technology uses machine learning and neural networks to process audio data and convert it into words that can be used in businesses.

What is the purpose of speech recognition AI ?

Speech recognition AI can be used for various purposes, including dictation and transcription. The technology is also used in voice assistants like Siri and Alexa.

What is speech communication in AI?

Speech communication is using speech recognition and speech synthesis to communicate with a computer. Speech recognition can allow users to dictate text into a program, saving time compared to typing it out. Speech synthesis is used for chatbots and voice assistants  like Siri and Alexa.

Which type of AI is used in speech recognition?

AI and machine learning are used in advanced speech recognition software, which processes speech through grammar, structure, and syntax.

What are the difficulties in voice recognition AI in artificial intelligence?

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Table of Contents

What are speech recognition systems?

How do speech recognition systems work, what are the benefits of speech recognition systems, what are the challenges of speech recognition systems, how can speech recognition systems be used in artificial intelligence, testing your speech model, how do speech recognition systems work: behind the scenes using ai.

Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

Speech recognition is becoming a popular “must have” feature. It has been around for over 50 years and has been developed by several companies in the United States, Europe, Japan and China. But what people don’t realize is that a lot of work goes on behind the scenes to make speech recognition systems both possible and practical.

Table of Contents What are speech recognition systems? How do speech recognition systems work? What are the benefits of speech recognition systems? What are the challenges of speech recognition systems? Voice Recognition Data Obstacles when Obtaining Voice Recognition Data How can speech recognition systems be used in artificial intelligence? Testing your speech model

Speech recognition is the process of translating human speech into a written format. Speech recognition technology is used in a wide variety of industries today. It is commonly confused with voice recognition. However, speech recognition technology has improved steadily over the years and it is now used to understand and process human speech.

Speech recognition technology has improved rapidly in recent years due to advancements in deep learning and big data. Advanced speech recognition solutions use AI and machine learning to understand and process human speech. Speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning and integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, speech recognition applications and devices learn as they go, evolving responses with each interaction.

Speech recognition can be customized for different purposes, such as language weighting and speaker labeling. Acoustics can be trained to improve accuracy. Speech recognition can be used in many different business scenarios where companies are making in roads in several areas of speech recognition.

Tip: To properly train speech recgognition systems, one needs a large amount of speech recordings with high diversity. You can get these various voice datasets from the crowd via clickworker. More about Voice Datasets

Language and acoustic modeling is the method via which speech recognition employs algorithms. The link between audio impulses and linguistic components of speech is represented via acoustic modeling. Language modeling, on the other hand, pairs word sequences with sounds to help separate similar-sounding words or phrases. Additionally, Hidden Markov Models , or HMMs, are frequently utilized to identify specific temporal speech patterns and thereby boost system accuracy. An HMM is a statistical model that depicts a system that evolves at random, with the assumption that changes in the future are independent of changes in the past.

The usage of N-grams with natural language processing is another technique for speech recognition. The complete speech recognition process is made simpler and takes less time to implement thanks to natural language processing, or NLP . N-grams, on the other hand, offer a more straightforward approach to language models and function by generating a probability distribution for a specific sequence. Finally, cutting-edge AI and machine learning technology will be included into the most sophisticated speech recognition software.

Video explaining how does speech recognition work

The benefits of speech recognition systems are an endlessly growing list, therefore contributing immensely to its popularity. The benefits mentioned below are the reason why speech recognition is a growing field in today’s day and age, and why everyone is keen on knowing how speech recognition systems work.

1. Benefits of speech recognition include faster operations, improved accuracy, and increased efficiency.

Speech recognition software is designed to be faster and more accurate than human beings. This means that it can be used to automate business processes and provide instant insights into what is happening in phone calls. The technology is also more accurate than a human and costs less per minute. Additionally, speech recognition software is readily accessible and easy to use.

2. Speech recognition systems can increase efficiency, create happy customers and maintain good levels of accuracy.

Speech recognition technology can help reduce errors, improve customer satisfaction, and speed up processes in a variety of industries. In healthcare settings, speech recognition is used to capture and log patient diagnoses and treatment notes. This can help reduce customer wait times and improve satisfaction. In call centers, speech recognition can be used to transcribe phone calls quickly and accurately. This can save time and improve the efficiency of the call center. Speech recognition can also be used as part of security protocols to resolve issues for customers more quickly. Overall, speech recognition technology can help reduce errors, improve customer satisfaction, and speed up processes.

3. In addition, speech recognition can help you create a more efficient and effective work environment.

Speech recognition software is more accurate and faster than a human, meaning it’s more cost-effective than using a human. In addition, speech recognition can be used to automate business processes and provide instant insights into call activity. This technology is also more accurate and efficient than human transcription.

Though speech recognition systems come with a lot of benefits and applications, there are quite a few challenges also present due to the complexity of this software.

1. The lack of standardization of speech

The lack of standardization in speech creates challenges for speech recognition because different people speak differently depending on their region, age, gender, and native language. Developers of speech recognition tools should take this into account and publicly report their progress to help ensure a equitable development process.

2. The different accents and pronunciations of words

Different accents and pronunciations can impact speech recognition technology in a number of ways. First, different accents can make it difficult for the software to understand what is being said. This is because the software is programmed to recognize certain sounds and patterns associated with specific words. When someone speaks with a different accent, those sound patterns can be altered, making it more difficult for the software to correctly identify the word.

Second, different dialects of a language can also impact speech recognition accuracy. This is because each dialect has its own unique way of pronouncing words and phrases. When speech recognition software is not programmed to account for these differences, it can lead to errors in recognition.

Finally, research has shown that accent and pronunciation can also affect accuracy rates for individual users. Speech recognition technology may be less effective for people who speak with an accent or dialect that is not well-represented in the data used to create the software.

Video on different accents around the world

3. The different speeds of speech

Speech recognition is the process of converting spoken words into text. It is a complex task for machines, as it can be affected by many factors, such as background noise, echoes, and different speeds of speech. The accuracy of speech recognition varies depending on these factors. For example, different speeds of speech can impact the accuracy of speech recognition. If a person speaks too quickly, the machine may not be able to understand all the words that are spoken. If a person speaks too slowly, the machine may have difficulty understanding the structure of the sentence. The accuracy of speech recognition also increases with vocabulary size and speaker independence. Therefore, different speeds of speech can impact speech recognition in terms of accuracy and processing speed.

4. The different noise levels in different environments

Speech recognition technology is complex, and it is still accurate even in noisy environments. However, noise levels can impact speech recognition accuracy. Background noise can easily throw a speech recognition device off track. Engineers have to program the device to filter out ambient noises and turn them into text that the software can understand. Recording tools can also have a significant impact on speech recognition accuracy. Customized data collection projects are often needed to overcome recording challenges. Voiceover artists can be recruited to record specific phrases or in-field collection can be used to collect speech in a more real-world scenario.

5. The different types of speech

Different types of speech can have an impact on speech recognition accuracy. For example, pronunciation can be a factor, as well as the type of speech (monotonic, disordered, etc.). Additionally, the complexity of the sound signal can impact accuracy.

One way to improve recognition accuracy is by taking into consideration the different types of speech and making decisions probabilistically at lower levels. This allows for more deterministic decisions to be made only at the highest level. Another way to improve accuracy is by expanding the complexity of sounds through neural networks.

6. The different context in which speech is used

7. the different purposes of speech.

The different purposes of speech affect speech recognition in a few ways. First, well-designed speech recognition software is easy to use and often runs in the background. Second, speech recognition software that incorporates AI becomes more effective over time as it accumulates data about human speech. Finally, the different purposes of speech can affect the accuracy of the software. For example, if someone is speaking to entertain, they may use more slang or talk faster, which can make it harder for the software to understand.

Voice Recognition Data

Voice recognition data comprises audio recordings collected from various sources, capturing spoken language or vocal utterances. This data serves as the foundation for training and developing voice recognition systems, enabling them to accurately interpret and transcribe human speech into text.

Voice recognition data usually comprise of conversations, speeches, or scripted dialogues. These recordings can encompass a diverse range of languages, accents, and speaking styles to ensure the robustness and adaptability of the voice recognition system.

Once obtained, the voice recognition data undergoes preprocessing, which involves tasks like noise reduction, speech segmentation, and feature extraction to enhance the quality and relevance of the audio samples. Subsequently, the processed data is used to train machine learning algorithms, deep neural networks, or other models capable of recognizing speech patterns and converting audio input into text output accurately.

Voice recognition data plays a pivotal role in the development of voice-controlled devices, virtual assistants, speech-to-text transcription systems, and various applications in industries such as telecommunications, automotive, healthcare, and consumer electronics. Its widespread use underscores the importance of high-quality, diverse datasets in advancing the capabilities of voice recognition technology.

Obstacles when Obtaining Voice Recognition Data

• Data Privacy and Security Concerns : Collecting audio data raises privacy concerns as it involves recording individuals’ voices, potentially without their explicit consent. Ensuring compliance with data protection regulations such as GDPR or HIPAA is crucial to avoid legal issues and maintain trust with users.
• Ethical Considerations : There are ethical considerations regarding the collection and use of voice data, particularly in terms of transparency, consent, and potential biases. Companies must establish ethical guidelines for data collection and usage to address concerns related to user consent, data anonymization, and fair treatment of individuals.
• Data Quality and Diversity : Acquiring high-quality and diverse voice data can be challenging. Variability in accents, languages, speech styles, and environmental conditions must be accounted for to develop robust and inclusive voice recognition systems. Ensuring representation across demographics and contexts is essential to mitigate biases and improve system performance for all users.
• Cost and Resource Constraints : Gathering large-scale voice datasets requires significant resources, including equipment, personnel, and infrastructure for data collection, storage, and processing. Companies must allocate sufficient budget and manpower to manage the entire data collection pipeline effectively.
• User Trust and Adoption : Building user trust is critical for successful data collection efforts. Companies need to communicate transparently about their data collection practices, address privacy concerns, and provide clear benefits to users to encourage participation. Ensuring a positive user experience during data collection can foster trust and increase user adoption rates.

Navigating these challenges requires careful planning, adherence to ethical principles, and proactive measures to address privacy, security, and quality concerns throughout the data collection process.

The use of virtual personal assistants and speech recognition technology has fast spread from our cellphones to our homes, and its applications in sectors including business, finance, marketing, and healthcare are starting to become clearer.

AI for speech recognition systems in communications

The largest benefit that speech recognition technology can offer the telecommunications sector is around conversational AI, like it does for many other sectors. These voice recognition systems enhance and add value to currently available telecommunication services because they can detect and engage in casual conversation and increasingly understand human speech. Additionally, it helps to strengthen targeted marketing initiatives, enable self-service, and better the entire customer experience.

The time it takes for customers to find what they need is reduced, and frequently they may sign up for new services or add-ons without even speaking to a human. All of the above are made easier with the use of self-service virtual assistants that are driven by speech recognition technology.

AI for speech recognition systems in banking

Security and customer experience are currently top objectives for customers in banking. Both can benefit from the application of AI in banking , especially speech recognition systems.

Many institutions use speech recognition to facilitate payments in mobile and online banking from a security standpoint. A common use case for voice authentication in mobile banking applications is to provide consumers with a simple means of identity verification in addition to complex passwords and 2-factor authentication procedures without the usual headache.

From the perspective of customer service, utilizing speech recognition to do mobile banking and handle customer service issues results in a simplified procedure because customers don’t have to wait in long service or support queues to speak to human agents for very simple resolutions.

AI for speech recognition systems in healthcare sector

For healthcare professionals to spend less time on data entry and more time treating patients, speech recognition has become a crucial tool. It has made it easier to remotely check for symptoms, provide patients with vital information during times of great perplexity, and generally lessen the exposure of healthcare professionals while still enabling them to give their patients the care they need. Speech recognition has already contributed much to remote healthcare and will only become better.

Minimizing the amount of time spent on administrative tasks related to electronic health records, relieving some of the doctors’ workload related to time spent at the computer inputting data and allowing them to concentrate on the patient are one of the applications of AI. AI will improve its comprehension of common and medical vocabulary, speaking patterns, etc. as speech recognition technology becomes more specialized. This will open the door for more sophisticated note-taking that will require less data entry while still recording important patient information.

The most crucial component of an effective speech recognition system is high-quality data, as you the output solely depends on the input. Therefore, the next step in ensuring that your system is ready to operate to its highest potential is choosing the appropriate training data.

Where can I find data on speech recognition systems?

In today’s world, data is now contextualized with the process and the agents who contributed to it rather than being inaccessible.

In order to maximize diversity and train models that speak to everyone, everywhere, known contributors can be actively sought. Or to put it another way, we can gather and evaluate audio datasets with a wide range of demographics by leveraging a varied population.

FAQs on Speech Recognition Systems and how it works

Advanced speech recognition solutions use AI and machine learning to understand and process human speech. These applications are able to learn as they go, and get better with each interaction. Speech recognition systems can be customized to recognize specific details about a person's voice, which helps to improve accuracy. Acoustics training can also be used to improve the quality of speech recognition by focusing on sound effects and voice environments. Speech recognition is used to understand and interpret human speech, and is constantly improving at a rapid pace.

What is the history of speech recognition?

Speech recognition technology has been around for a long time. The history of speech recognition technology can be traced back to the early 1900s. In the early days, research was focused on emulating the way the human brain processes and understands speech. This approach was later replaced by more statistical modeling techniques, like HMMs (Hidden Markov Models). HMMs were controversial in the early days, but they have since become the dominant speech recognition algorithm. Today, speech recognition technology is widely used across many industries, including finance and retail.

What are the main components of a speech recognition system?

A speech recognition system has three main components: the acoustic model, language model, and lexicon. The acoustic model is used to improve precision by weighting specific words that are spoken frequently. The language model helps the system to understand and process different types of spoken language. The lexicon is a database of words and phrases that the system can recognize such as voice recognition data

What are the different types of speech recognition systems?

• Automatic speech recognition is the most common type and is usually accurate. However, it can struggle with accents or noise.
• Visual speech recognition can identify objects and people more accurately than automatic speech recognition, but it can be slower.
• Robust speech recognition can handle difficult accents and noise better than visual or automatic speech recognition, but it may be slower.

What are some common applications of speech recognition?

Speech recognition is a versatile technology that is being used in an increasing number of applications. Common applications include mobile devices, word processing programs, language instruction, customer service, healthcare records, disability assistance, court reporting, and hands-free communication. Speech recognition can save time and lives in a variety of industries. The technology is becoming more ubiquitous and integrated into our lives as it becomes more refined.

What is the future of speech recognition systems?

The future of speech recognition technology is focused on ensuring pilots can spend more time on the mission. The demand for speech to text and text to speech services is fuelled by the need to make content available in many different formats. The medical field is using speech recognition technology to update patients' records in real-time. Speech recognition technology is growing in popularity, especially among white-collar workers. The development of the IoT and big data are going to lead to even deeper uptake of speech recognition technology.

How can I get started with speech recognition systems?

If you want to start using speech recognition, you need to install the SpeechRecognition library. You can install it using pip or by downloading and extracting the source code. The library has support for several different engines and APIs. To get started with speech recognition, try out the different tools listed in the Requirements section.

What are some common speech recognition software programs?

Speech recognition software programs are used to help machines understand human speech. These programs often have features that customize the program to the user's needs, such as language weighting and acoustic training, which can improve accuracy and performance. Additionally, speech recognition software can be equipped with filters to identify profanity and other undesirable words. Some advanced speech recognition solutions use artificial intelligence (AI) and machine learning to better understand human speech. As speech recognition technology advances, it is becoming more sophisticated in its ability to understand the complexities of human conversation.

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What Is Speech Recognition and How Does It Work?

With modern devices, you can check the weather, place an order, make a call, and play your favorite song entirely hands-free. Giving voice commands to your gadgets makes it incredibly easy to multitask and handle daily chores. It’s all possible thanks to speech recognition technology.

Let’s explore speech recognition further to understand how it has evolved, how it works, and where it’s used today.

What Is Speech Recognition?

Speech recognition is the capacity of a computer to convert human speech into written text. Also known as automatic/automated speech recognition (ASR) and speech to text (STT), it’s a subfield of computer science and computational linguistics. Today, this technology has evolved to the point where machines can understand natural speech in different languages, dialects, accents, and speech patterns.

Speech Recognition vs. Voice Recognition

Although similar, speech and voice recognition are not the same technology. Here’s a breakdown below.

Speech recognition aims to identify spoken words and turn them into written text, in contrast to voice recognition which identifies an individual’s voice. Essentially, voice recognition recognizes the speaker, while speech recognition recognizes the words that have been spoken. Voice recognition is often used for security reasons, such as voice biometrics. And speech recognition is implemented to identify spoken words, regardless of who the speaker is.

History of Speech Recognition

You might be surprised that the first speech recognition technology was created in the 1950s. Browsing through the history of the technology gives us interesting insights into how it has evolved, gradually increasing vocabulary size and processing speed.

1952: The first speech recognition software was “Audrey,” developed by Bell Labs, which could recognize spoken numbers from 0 to 9.

1960s: At the Radio Research Lab in Tokyo, Suzuki and Nakata built a machine able to recognize vowels.

1962: The next breakthrough was IBM’s “Shoebox,” which could identify 16 different words.

1976: The “ Harpy ” speech recognition system at Carnegie-Mellon University could understand over 1,000 words.

Mid-1980s: Fred Jelinek's research team developed a voice-activated typewriter, Tangora, with an expanded bandwidth of 20,000 words.

1992: Developed at Bell Labs, AT&T’s Voice Recognition Call Processing service was able to route phone calls without a human operator.

2007: Google started working on its first speech recognition software, which led to the creation of Google Voice Search in 2012.

2010s: Apple’s Siri and Amazon Alexa came into the scene, making speech recognition software easily available to the masses.

How Does Speech Recognition Work?

We’re used to the simplicity of operating a gadget through voice, but we’re usually unaware of the complex processes taking place behind the scenes.

Speech recognition systems incorporate linguistics, mathematics, deep learning, and statistics to process spoken language. The software uses statistical models or neural networks to convert the speech input into word output. The role of natural language processing (NLP) is also significant, as it’s implemented to return relevant text to the given voice command.

Computers go through the following steps to interpret human speech:

• The microphone translates sound vibrations into electrical signals.
• The computer then digitizes the received signals.
• Speech recognition software analyzes digital signals to identify sounds and distinguish phonemes (the smallest units of speech).
• Algorithms match the signals with suitable text that represents the sounds.

This process gets more complicated when you account for background noise, context, accents, slang, cross talk, and other influencing factors. With the application of artificial intelligence and machine learning , speech recognition technology processes voice interactions to improve performance and precision over time.

Speech Recognition Key Features

Here are the key features that enable speech recognition systems to function:

• Language weighting: This feature gives weight to certain words and phrases over others to better respond in a given context. For instance, you can train the software to pay attention to industry or product-specific words.
• Speaker labeling: It labels all speakers in a group conversation to note their individual contributions.
• Profanity filtering: Recognizes and filters inappropriate words to disallow unwanted language.
• Acoustics training: Distinguishes ambient noise, speaker style, pace, and volume to tune out distractions. This feature comes in handy in busy call centers and office spaces.

Speech Recognition Benefits

Speech recognition has various advantages to offer to businesses and individuals alike. Below are just a few of them.

Faster Communication

Communicating through voice rather than typing every individual letter speeds up the process significantly. This is true both for interpersonal and human-to-machine communication. Think about how often you turn to your phone assistant to send a text message or make a call.

Multitasking

Completing actions hands-free gives us the opportunity to handle multiple tasks at once, which is a huge benefit in our busy, fast-paced lives. Voice search , for example, allows us to look up information anytime, anywhere, and even have the assistant read out the text for us.

Aid for Hearing and Visual Impairments

Speech-to-text and text-to-speech systems are of substantial importance to people with visual impairments. Similarly, users with hearing difficulties rely on audio transcription software to understand speech. Tools like Google Meet can even provide captions in different languages by translating the speech in real-time.

Real-Life Applications of Speech Recognition

The practical applications of speech recognition span various industries and areas of life. Speech recognition has become prominent both in personal and business use.

• Technology: Mobile assistants, smart home devices, and self-driving cars have ceased to be sci-fi fantasies thanks to the advancement of speech recognition technology. Apple, Google, Microsoft, Amazon, and many others have succeeded in building powerful software that’s now closely integrated into our daily lives.
• Education: The easy conversion between verbal and written language aids students in learning information in their preferred format. Speech recognition assists with many academic tasks, from planning and completing assignments to practicing new languages.
• Customer Service: Virtual assistants capable of speech recognition can process spoken queries from customers and identify the intent. Hoory is an example of an assistant that converts speech to text and vice versa to listen to user questions and read responses out loud.

Speech Recognition Summarized

Speech recognition allows us to operate and communicate with machines through voice. Behind the scenes, there are complex speech recognition algorithms that enable such interactions. As the algorithms become more sophisticated, we get better software that recognizes various speech patterns, dialects, and even languages.

Faster communication, hands-free operations, and hearing/visual impairment aid are some of the technology's biggest impacts. But there’s much more to expect from speech-activated software, considering the incredible rate at which it keeps growing.

How Does Speech Recognition Work? Which Algorithm is Used in Speech Recognition?

In today’s technology-driven world, everything is based on different modes of technology. Whether its an automated text recognition or a robotic voice translation, technological advancement has set the standard high. 

Today, you communicate with most of the big companies and instead of a person, an automated voice instructs you to press buttons and navigate through an option menu. 

Often your android phones have Google Assistant to solve all the queries. The system which makes the entire scene work out is known as a speech recognition system .  

How Does Speech Recognition System Work?

Speech Recognition works on human inputs that enable machines to react on inserted text, voice, or any other inputs. You can use speech recognition software at home and for businesses.

A certain range of software products allows users to dictate to their computers or on phones so that their words get converted to a text in a word processing or email document.

Which Algorithm is Used in Speech Recognition?

The algorithms used in this form of technology include PLP features, Viterbi search, deep neural networks, discrimination training, WFST framework, etc. If you are interested in Google’s new inventions, keep checking their recent publications on speech. The algorithms used by Google are available in an open-source format.

Is Speech Recognition a Machine Learning?

It would be better if we say machine learning groups are using speech recognition along with voice synthesis to bring in the power of input recognition for the benefit of all.

Speech is powerful which brings a human dimension to different electronic devices. In the present-day world, cloud-based computers are used by people that can be controlled by voice, offering conversational responses to a wide range of queries.

Speech recognition training allows AI models to understand unique inputs present in the recorded audio data. Machine learning has still a long way to achieve perfection in many cases.

The software is programmed in such a way that it entirely covers up all nuances present in human speech like speech length, voice pattern, tone frequency, etc.

However, to properly train a speech recognition system, you need to provide quality information for processing the input out there.

These forms of systems are highly beneficial for people with disabilities . If a person has lost the use of his hands or visually impaired then they can make use of automatic speech recognition or advanced voice recognition to make natural voice recognition work.

Advantages of the Speech Recognition System

Makes work processes more efficient.

Through the use of speech recognition, document processing becomes shorter and efficient. Documents can be generated within a short period quicker and faster than ever before as they are typed. The software also saves a great deal of employment of labor for documentation work.

Playing Back Simple Information

Nowadays customers want to have fast access to their queries. In many circumstances, customers do not want to speak to an operator. That moment, speech recognition can be used to provide basic information to the user.

Helping Aid for Visually and Hearing Impaired

People with visual and hearing impairments can highly rely on screen readers along with text-to-speech dictation systems. This software can help to convert audio into text which is regarded as critical for people having visual and hearing impairments.

Enables Hands-Free Communication

When your eyes and hands are unable to interact, then speech becomes incredibly powerful. Devices like Amazon’s Alexa, Apple’s Siri or Google Maps come to rescue to reduce misinterpreted navigation or communication.

How to Leverage an API?

API stands for Application Programming Interface(API). It is a set of programming instructions for accessing a web-based tool or software. A software company usually releases its API to the public so that the software developers can design products powered by its service. An API is basically a software-to-software interface and not a user programming interface.

As speech recognition converts speech to text, there are many machine learning like Python, API’s, Google Cloud Speech API, that helps in a speech to text, text to speech, audio dictation along with automated voice generator.

Categories under which Speech Recognition Work

There are normally two domains under which speech recognition works which are small vocabulary and a large vocabulary.

Many Users Small Vocabulary

These systems are best for automatic telephone answering activity. The users can speak with a lot of variations in accent and speech patterns, yet the system will understand them. However, the usage is limited to a small number of inputs such as basic menu options.

Limited Users Large Vocabulary

These systems are ideal for a business environment where a small number of users can work with the program. The system works with a good level of accuracy and has a vocabulary in thousands. You are required to train the system so that it works best with a small number of primary users.

Brief about Text-To-Speech Technology

Text-To-Speech is a type of technology that can assist to read aloud digital text. It is often known as “read aloud” technology for its functionality.

With just a click of a button, TTS can take words on a digital device and can convert them into audio. TTS is very useful for kids and disables persons who struggle with reading. It can also help kids with writing and editing skills for school projects.

TTS can work on nearly every digital device with all kinds of text files such as word page documents, online web pages, etc. The voice on TTS is computer generated and the reading speed can be adjusted depending upon your need.

Text-To-Speech has many applications across different fields. It can increase user engagement, accessibility for high-end productive results.

Indian TTS is an Indian based startup working for developing AI embedded skills into speech recognition products so that reading and writing never becomes a hurdle in anyone’s life.

The start-up has a built-in aim to foster an environment where the interface of different electronic devices becomes user-friendly.

With audio dictation software and Interactive Voice Response(IVR), the startup tries to increase customer engagement, recognizes natural accent/voice to integrate real-time solutions.

It aims to offer the latest speech technology with both offline TTS/ASR solutions. The products have embedded speech recognition in different languages that include Bengali, Hindi, Kannada, Tamil, Gujarati, and more.

NVDA Screen Reader for Visually Impaired

NVDA that stands for Non-Visual Desktop Access is a non-paid screen reader that enables a visually impaired person to use different electronic devices. It reads aloud the text on the screen in a robotic voice.

You just need to install it into your computer and can plug in the Indian TTS addon in NVDA. It can detect both male and female voices. It helps in browsing the web, reading, and writing, sending and receiving emails, panel applets, and other generic tasks.

NVDA makes the life of a disabled person easy to a large extent by providing them technical support in terms of disability. It opens up doors for them to explore the world of reading and writing similar to any other non-disable a person.

Now and then technology is changing rapidly and so is the speech recognition technology. A lot of research work in the proper direction is needed to harness the ultimate benefits of speech technology.

Speech Recognition can turn into the next big thing in the field of communication, business, health, tourism, and more. The in-depth research work can help everyone to grow and adapt to different algorithms for future benefits. What is needed is deep monitoring and analysis of different upcoming changes in the field of human interaction.

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Windows 11 speech recognition feature gets ditched in September 2024 – but only because there’s something better

Voice Access trumps Windows Speech Recognition in almost every way

Windows 11 ’s voice functionality is being fully switched over to the new Voice Access feature later this year, and we now have a date for when the old system – Windows Speech Recognition (WSR) – will be officially ditched from the OS.

The date for the replacement of WSR by Voice Access has been announced as September 2024 in a Microsoft support document (as Windows Latest noticed). Note that the change will be ‘starting’ in that month, so will take further time to roll out to all Windows 11 PCs.

However, there’s a wrinkle here, in that this is the case for Windows 11 22H2 and 23H2 users, which means those still on Windows 11 21H2 – the original version of the OS – won’t have WSR removed from their system.

Windows 10 users will still have WSR, of course, as Voice Access is a Windows 11-only feature.

Analysis: WSR to go MIA, but it’s A-OK (for the most part)

This move is no surprise as Microsoft removed Windows Speech Recognition from Windows 11 preview builds back at the end of 2023. So, this change was always going to come through for release versions of Windows 11, it was just a question of when – and now we know.

Will the jettisoning of WSR mean this feature is missed by Windows 11 users? Well, no, not really, because its replacement, Voice Access, is so much better in pretty much every respect . It is leaps and bounds ahead of WSR, in fact, with useful new features being added all the time – such as the ability to concoct your own customized voice shortcuts (a real timesaver).

In that respect, there’s no real need to worry about the transition from WSR to Voice Access – the only potential thorny issue comes with language support. WSR offers a whole lot more in this respect, because it has been around a long time.

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However, Voice Access is getting more languages added in the Moment 5 update . And in six months’ time, when WSR is officially canned (or that process begins), we’ll probably have Windows 11 24H2 rolling out, or it’ll be imminent, and we’d expect Voice Access to have its language roster even more filled out at the point.

Those on Windows 11 21H2 will be able to stick with WSR as observed, but then there’s only a very small niche of users left on that OS, as Microsoft has been rolling out an automatic forced upgrade for 21H2 for some time now. ( Indeed, this is now happening for 22H2 as of a few weeks ago). Barely anyone should remain on 21H2 at this point, we’d imagine, and those who are might be stuck there due to a Windows update bug, or oversight during the automated rollout.

Windows 10 users will continue with WSR as it’s their only option, but as a deprecated feature, it won’t receive any further work or upgrades going forward. That’s another good reason why Windows 11 users should want to upgrade to Voice Access which is being actively developed at quite some pace.

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Darren is a freelancer writing news and features for TechRadar (and occasionally T3) across a broad range of computing topics including CPUs, GPUs, various other hardware, VPNs, antivirus and more. He has written about tech for the best part of three decades, and writes books in his spare time (his debut novel - 'I Know What You Did Last Supper' - was published by Hachette UK in 2013).

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