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Reseach Article

Sub vocal Speech Recognition System based on EMG Signals

Published on September 2015 by Yukti Bandi, Riddhi Sangani, Aayush Shah, Amit Pandey, Arun Varia
CAE Proceedings on International Conference on Communication Technology
Foundation of Computer Science USA
ICCT2015 - Number 7
September 2015
Authors: Yukti Bandi, Riddhi Sangani, Aayush Shah, Amit Pandey, Arun Varia
21b05297-faba-4c37-8ec2-421f3df1e95f

Yukti Bandi, Riddhi Sangani, Aayush Shah, Amit Pandey, Arun Varia . Sub vocal Speech Recognition System based on EMG Signals. CAE Proceedings on International Conference on Communication Technology. ICCT2015, 7 (September 2015), 31-35.

@article{
author = { Yukti Bandi, Riddhi Sangani, Aayush Shah, Amit Pandey, Arun Varia },
title = { Sub vocal Speech Recognition System based on EMG Signals },
journal = { CAE Proceedings on International Conference on Communication Technology },
issue_date = { September 2015 },
volume = { ICCT2015 },
number = { 7 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 31-35 },
numpages = 5,
url = { /proceedings/icct2015/number7/22685-1592/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 CAE Proceedings on International Conference on Communication Technology
%A Yukti Bandi
%A Riddhi Sangani
%A Aayush Shah
%A Amit Pandey
%A Arun Varia
%T Sub vocal Speech Recognition System based on EMG Signals
%J CAE Proceedings on International Conference on Communication Technology
%@ 0975-8887
%V ICCT2015
%N 7
%P 31-35
%D 2015
%I International Journal of Computer Applications
Abstract

This paper presents results of electromyography (EMG) speech recognition which captures the electric potentials that are generated by the human articulatory muscles. EMG speech recognition holds promise for mitigating the effects of high acoustic noise on speech intelligibility in communication systems. Few words have been collected from EMG from a male subject, speaking normally and sub vocally. The collected signals are then required to be filtered and transformed into features using Wavelet Packet and statistical windowing techniques. Finally, the concept of neural network with back propagation method has been used for classification of data. Using windowed signals and the trained neural network an arduino operated bot was controlled as an application to demonstrate the future scope of the paper. The success rate was 73%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Emg Sub Vocal Speech Neural Network Electromyography