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Feature Extraction and Recognition of Hindi Spoken Words using Neural Networks

by Poonam Sharma, Anjali Garg
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 7
Year of Publication: 2016
Authors: Poonam Sharma, Anjali Garg
10.5120/ijca2016909870

Poonam Sharma, Anjali Garg . Feature Extraction and Recognition of Hindi Spoken Words using Neural Networks. International Journal of Computer Applications. 142, 7 ( May 2016), 12-17. DOI=10.5120/ijca2016909870

@article{ 10.5120/ijca2016909870,
author = { Poonam Sharma, Anjali Garg },
title = { Feature Extraction and Recognition of Hindi Spoken Words using Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 7 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number7/24907-2016909870/ },
doi = { 10.5120/ijca2016909870 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:44:19.505833+05:30
%A Poonam Sharma
%A Anjali Garg
%T Feature Extraction and Recognition of Hindi Spoken Words using Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 7
%P 12-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic Speech Recognition System has been a challenging and interesting area of research in last decades. Only a few researchers have worked on Hindi and other Indian languages. In this paper, a Speech Recognition System for Hindi language based on MFCC, PLP and neural networks is proposed and it was observed that the accuracy of the system was better than other conventional methods.

References
  1. Vibha Tiwari. 2010. MFCC and its applications in speaker recognition. International Journal on Emerging Technologies 1(1): (2010), pp. 19-22.
  2. Lindasalwa Muda, Mumtaj Begam and I. Elamvazuthi,. 2010. Voice recognition algorithm using MFCC & DTW techniques. Journal Of Computing, Volume 2, Issue 3, March 2010, ISSN 2151-9617, pp. 138-143.
  3. Ripul Gupta. Speech recognition for Hindi. Indian Institute of Technology, Bombay, pp. 11-14.
  4. Yuan Meng. 2004. Speech recognition on DSP: Algorithm optimization and performance analysis. The Chinese university of Hong Kong, July 2004, pp. 1-18.
  5. Sirko Molau, Michael Pitz, Ralf Schl¨uter, and Hermann Ney. Computing Mel-frequency cepstral coefficients on the power spectrum. University of Technology, 52056 Aachen, Germany
  6. Siddhant C. Joshi, Dr. A.N.Cheeran. 2014. MATLAB Based Feature Extraction Using Mel Frequency Cepstrum Coefficients for Automatic Speech Recognition. International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 6, June 2014
  7. Namrata Dave. 2013. Feature Extraction Methods LPC, PLP and MFCC In Speech Recognition. www.ijaret.org Volume 1, Issue VI, July 2013
  8. H. Hermansky and N. Morgan. 1994. RASTA processing of speech. IEEE Trans. on Speech and Audio Proc., vol. 2, no. 4, pp. 578-589, Oct. 1994
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Speech Recognition Mel frequency Cepstral Coefficient Predictive Linear Coding

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