literature survey on speech recognition

Automatic Speech Recognition: Systematic Literature Review

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Help | Advanced Search

Electrical Engineering and Systems Science > Audio and Speech Processing

Title: end-to-end speech recognition: a survey.

Abstract: In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning. In the wake of this transition, a number of all-neural ASR architectures were introduced. These so-called end-to-end (E2E) models provide highly integrated, completely neural ASR models, which rely strongly on general machine learning knowledge, learn more consistently from data, while depending less on ASR domain-specific experience. The success and enthusiastic adoption of deep learning accompanied by more generic model architectures lead to E2E models now becoming the prominent ASR approach. The goal of this survey is to provide a taxonomy of E2E ASR models and corresponding improvements, and to discuss their properties and their relation to the classical hidden Markov model (HMM) based ASR architecture. All relevant aspects of E2E ASR are covered in this work: modeling, training, decoding, and external language model integration, accompanied by discussions of performance and deployment opportunities, as well as an outlook into potential future developments.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Speech emotion recognition methods: A literature review

[email protected]

[email protected]

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Reprints and Permissions
• Cite Icon Cite
• Search Site

Babak Basharirad , Mohammadreza Moradhaseli; Speech emotion recognition methods: A literature review. AIP Conf. Proc. 3 October 2017; 1891 (1): 020105. https://doi.org/10.1063/1.5005438

Download citation file:

• Ris (Zotero)
• Reference Manager

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

Citing articles via

Publish with us - request a quote.

Sign up for alerts

• Online ISSN 1551-7616
• Print ISSN 0094-243X
• For Researchers
• For Librarians
• For Advertisers
• Our Publishing Partners  
• Physics Today
• Conference Proceedings
• Special Topics

pubs.aip.org

• Privacy Policy
• Terms of Use

Connect with AIP Publishing

This feature is available to subscribers only.

Sign In or Create an Account

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

LITERATURE SURVEY – SPEECH RECOGINATION AND PREPROCESSING

Automatic speech Recognition(ASR) is more important in the information and communication technology. ASR is used to fill a form, Frist Spoken can be converted into text(STT). The audio noise can be removed by MCFF Algorithm. The audio can be converted into corresponding English characters by using Dynamic Bayesian Network (DBN) and then it can be visualized in the Form format. It will recognize all Accents using learning method and stored in Knowledge base. People who are uneducated and blindfor them it would be helpful. Their audio can be captured from the environment and then the noise can be removed from the audio. After that speech can be coded as characters. Atlast From will be generated in Format.

Related Papers

IJESRT Journal

This paper presents the approach for Hindi fruit name recognizer system. Every person has its uniqueness in his speech. So in this approach the database speech samples are collected from different 20 speakers with two iterations. These recordings are used to train by acoustic model. This model is trained on 20 speaker database having vocabulary size is 45 words. HTK toolkit is used to train the input data and evaluation of the results. The proposed system gives a recognition rate of 94.28% for sentence and 98.09 for word level. KEYWORDS: HMM (Hidden Markov Model), ASR (Automatic Speech Recognition), Speech Recognition (SR). MFCC (Mel Frequency Cepstral Coefficient)

Abhishek Thakur

Naveen Kumar

This paper presents a study of automatic speech recognition system for Hindi utterances with regional Indian accents. In paper [3] we have designed matlab based ASR and control system for eight English key words by using simple rule base. This rule base algorithm is the beginning stage for Key Word recognition. In paper [4] we have designed Design of Hindi Key Word Recognition System for Home Automation System Using MFCC and DTW. Features of the speech signal are extracted in the form of MFCC coefficients and Dynamic Time Warping (DTW) has been used as features matching techniques. The recognition results are tested for clean and noisy test data. Average accuracy for clean data is 97.50 % while that for noisy data is 91.25 %. We face problem in noise environment to detect correct utterance now we are going to review different papers and find out different techniques to design our ASR control system for Hindi Key Words using MFCC and DTW in noise environment.

International Journal of Electrical and Computer Engineering (IJECE)

Hilman F. Pardede

In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature t...

Mayank Dave

Bert Cranen

Automatic speech processing systems are employed more and more often in real environments. Although the underlying speech technology is mostly language independent, differences between languages with respect to their structure and grammar have substantial effect on the recognition systems performance. In this paper, we present a review of the latest developments in the sign language recognition research in general and in the Arabic sign language (ArSL) in specific. This paper also presents a general framework for improving the deaf community communication with the hearing people that is called SignsWorld. The overall goal of the SignsWorld project is to develop a vision-based technology for recognizing and translating continuous Arabic sign language ArSL.

Gaurav Leekha

delowar sikder

The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of re...

In previous work we have shown that an ASR system consisting of a dual-input DBN which simultaneously observes MFCC acoustic features and predicted phone labels that are generated by an exemplar-based Sparse Classification (SC) system can achieve better word recognition accuracies in noise than a system observing only one of those input streams. This paper explores two modifications of the SC input to further improve the noise robustness of the dual-input DBN system: 1) integrating more time context and 2) using N best states. Experiments on AURORA-2 reveal that the first approach significantly improves the recognition results at almost all SNRs, but particularly in the more noisy conditions, achieving up to 6.1% (absolute) accuracy gain at SNR −5 dB. The second modification shows that there is an optimal N which allows the maximum attainable accuracy to be even further improved with another 11.8% at −5 dB.

RELATED PAPERS

Journal of Computer Science IJCSIS

Tuomas Virtanen , Felix Weninger

Neural Computing and Applications

ismail shahin

IRJET Journal

Konstantin Markov

International Journal of Advance Research in Computer Science and Management Studies [IJARCSMS] ijarcsms.com

khalid Daoudi

vishal pasricha , Mayank Dave

Ahmad Yaar Zadran

Artificial Intelligence Review

Shima Tabibian

Asmaa EL HANNANI

Ijetrm Journal

Srikanth Bomma

International Journal of Engineering Sciences & Research Technology

Ijesrt Journal

SIDDHARTH S U K H D E V MORE

IOSR Journals publish within 3 days

Frank Rudzicz

Eslam ElMaghraby

International Journal of Engineering Research and

Archana Kadam

Proc. International Symposium on Natural …

Hasnat Md. Abul

Telecommunication Systems

International Conference on Acoustics, Speech, and Signal Processing

Björn Schuller

nadia khizar

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

Samir Rustamov

Computer Applications in Engineering Education

Ramzi Saifan

deepak gopinath

Elizabeth Sherly

International Journal of Computer Applications

Ratnadeep Deshmukh

ACM Transactions on Speech and Language Processing

Anton Batliner

pooja prajapati

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY

Ijariit Journal

Multimedia Systems

Anwar Hossain

International Journal of Advanced Computer Science and Applications

bousselmi souha

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

ICDSMLA 2019 pp 745–752 Cite as

A Study on Sign Language Recognition-A Literature Survey

• P. V. Naresh 37 ,
• R. Visalakshi 37 &
• B. Satyanarayana 38  
• Conference paper
• First Online: 19 May 2020

177 Accesses

1 Citations

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 601))

This survey primarily focuses on the recent development of technologies being used for hearing loss and speech loss people to converse easily and frequently with usual people. The work so far gone for the development introduced in various technologies and techniques such as smart gloves, android application, techniques such as Convolution Neural Networks, Gaussian filtering, HMM, speech to text, video to Text then to Speech etc. The system was tested with SSVM classifier to check the accuracy with 10 sample gestures and found that the average recognition rate was 95.4% accuracy. Hence I conclude based on the accuracy obtained in test results my future work will continue on SSVM classifier.

• S ign language
• Hand gesture
• Speech recognition
• Deaf and dumb

* No academic titles or descriptions of academic positions should be included in the addresses.

The affiliations should consist of the author’s institution, town/city, and country.

This is a preview of subscription content, log in via an institution .

Buying options

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Reda MM, Mohammed NG, Azeem RAR, Seoud A (2018) SVBicomm signs voice bidirectional communication systems for normal, deaf and dumb and blind people based on machine learning. In: 1st international conference on computer application and information security (ICCAIS). IEEE, Riyadh

Google Scholar  

Kumar S, Yadav G, Singh HP, Malhotra S, Gupta A (2018) SSVM clsssifier and hand gesture based sign language recognition. In: International conference on intelligent circuits and systems (ICICS). Phagwara

Tao W, Leu MC, Yin Z (2018) ASL alphabets recognition using conventional neural networks with multiview augmentation and inference fusion. Eng Appl Artif Intell 76:202–213

Ananth Rao G, Kishore PVV (2017) Selfie video based continuous Indian signs language recognition system. Ain Shams Eng J 9(4):1929–1939

Nath GG, Anu VS (2017) Embedded sign language interpreter system for deaf and dumb people. In: International conference on innovations in information embedded and communication systems (ICIIECS). IEEE, Coimbatore

ElBadawy M, Elons AS, Shedeed HA, Toolbar MF (2017) Arabic sign language recognition with 3D convolutionals neural networks. In: 8th international conference on intelligent computing and information systems (ICICIS). IEEE, Cairo

Patel U, Ambekar AG (2017) Moment based signs language recognition for Indian language. In: International conference on computing, communications, control and automation (ICCUBEA). IEEE, Pune

Gupta B, Shukla P, Mittal A (2016) K-nearest correlated neighbor’s classifications for indian sign language gesture recognition using feature fusion. In: International conference on computer communication and informatics (ICCCI). IEEE, Coimbatore

Abdulla D, Abdulla S, Manaf R, Anwar, Jardal H (2016) Design and implementation of sign-to-speech-text system for deaf and dumb people. In: 5th international conference on electronic devices, systems and applications (ICEDSA). IEEE, Ras Al Khaimah

Abdallah EE, Fayyoumi E (2016) Assistive technology for deaf people based on android Platform. Procedia Comput Sci 94:295–301

Hummadi FN, Al-Nuaimy (2017) Design and implementation of deaf and mute people interaction system. In: International conference on engineering and technology (ICET). IEEE, Antalya

El- Gayyar MM, Ibrahim AS, Wahed ME (2016) Translation from arabic speech to arabic signs language based on cloud computing. Egypt Inf J 17(3):295–303

Chen Y, Zhan W (2016) Research and implementation of sign language recognition method based on Kinect. In: 2nd international conference on computers and communications. IEEE, Chengdu

Hikawa H, Kaida K (2015) Novel FGPA implementation of sign recognition systems with som–hebb classifier. IEEE Trans Circuits Syst Video Technol 25(1):153–166

Tubaiz N, Shanableh T, Assaleh K (2015) Glove-based continuous Arabic sign language recognition in user-dependent mode. IEEE Trans Human-Mach Syst 45(4):526–533

Liang, W. Guixi L, Hongyan D (2015) Dynamic combine gestures recognition based on multi-feature fusion in a complex environment. J China Univ Posts Telecommun 22(2):81–88

Agarwal SC, Jalal AS, Bhatnagar C (2012) Recognition of Indian sign language using feature fusion. In: 4th international conference on intelligent human computer interaction (IHCI). IEEE, Kharagpur, pp 1–5

Nikam AS, Ambekar AG (2016) Sign language recongization using image based hand gesture recognition techniques. In: 3rd international conference on innovation in information, embedded and communication system (IC-GET). IEEE, Coimbatore

Bhuyan MK, Ghoah D, Bora PK (2006) A frame work of hand gesture recognition with applications to sign language. In: Annual IEEE india conference. IEEE, New Delhi, pp 1–6

Bauer B, Hienz H, Kraiss K-F (2000) Video-based continuous sign language recognition using statistical methods. 15th international conference on pattern recognition (ICPR). IEEE, Barcelona, pp 463–466

The Bangla Sign Language Community, the Deaf of Bangladesh, People and Language Detail Profile, Profile Year: 2012

Fang G, Gao W, Zhao D (2007) Large-vocabulary continuous sign language recognition based on transition-movement models. IEEE Syst Man Cybern Part A Syst Humans IEEE Trans 37(1):1–9

Ghotkar AS, Khatal R, Khupase S, Asati S, Hadap M (2012) Hand gesture recognition for Indian sign language. IEEE international conference on computer communication informatics, pp 1–4

Kumar MG, Gurjar MK, Singh MS (2016) American sign language translating glove using flex sensor. Imp J Interdiscip Res 2(6)

Mohandes M, Aliyu S, Deriche M (2014) Arabic sign language recognition using the leap motion controller. 23rd international symposium on industrial electronics (ISIE). Istanbul, IEEE, pp 960–965

Pankajakshan PC, Thilagavathi B (2015) Sign language recognition system. In: IEEE sponsored 2nd international conference on innovations in information embedded and communication systems (ICIIECS). IEEE, Coimbatore

Ravikiran J, Mahesh K, Mahishi S, Dheeraj R, Sudheender S, Pujari NV (2009) Finger detection for sign language recognition. In: Proceedings of the international multiconference of engineers and computer scientists, vol 1. Hong Kong

Thepade S, Narkhede A, Kelvekar P (2013) Novel technique for background removal from sign images for sign language recognition system. Int J Comput Appl 78(10), https://doi.org/10.5120/13523-1203

Starner T, Pentland A (1998) A real-time american sign language recognition using desk and wearable computer based video. IEEE Trans Pattern Anal Mach Intell 20(12):1371–1375

Singha J, Das K (2013) Indian sign language recognition using Eigen value weighted euclidean distance based classification technique. Int J Adv Comput Sci Appl (IJACSA) 4(2)

ZahoorZafrulla (2014) Automatic recognition of american sign language, PhD Dissertation, Georgia Institute of Technology, USA

Download references

Author information

Authors and affiliations.

Department of Computer Science & Engineering, Annamalai University, Annamalai Nagar, Chennai, India

P. V. Naresh & R. Visalakshi

Department of Computer Science & Engineering, CMR Institute of Technology, Hyderabad, India

B. Satyanarayana

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to P. V. Naresh .

Editor information

Editors and affiliations.

BioAxis DNA Research Centre Private Ltd, Hyderabad, Telangana, India

Polish Academy of Science, Systems Research Institute, Warsaw, Poland

Marcin Paprzycki

Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India

Vinit Kumar Gunjan

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Naresh, P.V., Visalakshi, R., Satyanarayana, B. (2020). A Study on Sign Language Recognition-A Literature Survey. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_80

Download citation

DOI : https://doi.org/10.1007/978-981-15-1420-3_80

Published : 19 May 2020

Publisher Name : Springer, Singapore

Print ISBN : 978-981-15-1419-7

Online ISBN : 978-981-15-1420-3

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research