CFP last date
20 June 2024
Reseach Article

Speaker Independent Kannada Speech Recognition using Vector quantization

Published on May 2012 by M. A. Anusuya, S. K. Katti
National Conference on Advancement in Electronics & Telecommunication Engineering
Foundation of Computer Science USA
NCAETE - Number 1
May 2012
Authors: M. A. Anusuya, S. K. Katti
38e7eea9-1ec5-4a79-aba7-3e7e9b6b4d5d

M. A. Anusuya, S. K. Katti . Speaker Independent Kannada Speech Recognition using Vector quantization. National Conference on Advancement in Electronics & Telecommunication Engineering. NCAETE, 1 (May 2012), 32-35.

@article{
author = { M. A. Anusuya, S. K. Katti },
title = { Speaker Independent Kannada Speech Recognition using Vector quantization },
journal = { National Conference on Advancement in Electronics & Telecommunication Engineering },
issue_date = { May 2012 },
volume = { NCAETE },
number = { 1 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 32-35 },
numpages = 4,
url = { /proceedings/ncaete/number1/6593-1083/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancement in Electronics & Telecommunication Engineering
%A M. A. Anusuya
%A S. K. Katti
%T Speaker Independent Kannada Speech Recognition using Vector quantization
%J National Conference on Advancement in Electronics & Telecommunication Engineering
%@ 0975-8887
%V NCAETE
%N 1
%P 32-35
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper a statistical method is used to remove the silence from the speech signal. This method is applied on vector quantization technique to identify the minimum speech patterns that are required while creating the training set of the speech samples. This paper also discusses the importance and efficiency of the algorithms used in vector quantization for the clustering purpose. Also speech recognition accuracies for speaker dependent and speaker independent methods have been evaluated and tabulated in the tables given below. The paper shows the importance of the statistical method analysis of the signal than the normal analysis.

References
  1. Ladefoged, Peter. "Elements of acoustic phonetics". 2rd ed. The University of Chicago Press. Chicago. 1996.
  2. Stranneby, Dag. "Digital Signal Processing: DSP and Applications". Oxford. 2001
  3. Claudio, D. ; Marins, "Calculo numerico computacional: teoriae pratica". 2rd ed. Editora Atlas. Sao Paulo. 1994.
  4. M. A. Anusuya and S. K. Katti, "Discrete Wavelet transform for Noisy kannada speech recognition", International journal of computational Intelligence Research, Vol6, No. 4, Published by Research India publication, India,2010.
  5. G. Saha, Sandipan et. al. , " A New Silence Removal and Endpoint Detection Algorithm for Speech and Speaker Recognition Applications", Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Khragpur, Kharagpur-721 302, India.
  6. E- Hocine Bourouba et. al. " Isolated Words Recognition System Based on Hybrid Approach DTW/GHMM" Department of electronic Faculty of Engineering, University of Annaba, Algeria Automatic and Signals Laboratory
  7. M. A. Anusuya and S. K. Katti, " Speech Recognition by Machine : A Review", International Journal of Computer Science and Information Security, Vol. 6, No. 3, 2009.
  8. Rabiner L. , B. -H. Juang, " Fundamentals of Speech Recognition". Prentice Hall,1993.
  9. Vintsyuk T. K. , "Speech Discrimination by Dynamic Programming". Kibernetika, 4(2), 81–88,1968.
  10. Velichko V. M. , N. G. Zagoruyko, "Automatic Recognition of 200 Words", Int. J. Man-Machine Studies, 2, 223, 1970.
  11. Rabiner L. R. , "A Tutorial on hidden markov models and selected applications in speech recognition",Proc. IEEE, 77(2), 257–289,1989.
  12. Rabiner L. , B. . H. Juang ,"Fundamentals of Speech Recognition. Prentice Hall, 1993.
  13. Lipeika A. , J. Lipeikien?e, "Speaker identification using vector quantization". Informatica, 6(2), 167–180, 1995.
  14. Lipeika A. , J. Lipeikien?e, "Speaker identification methods based on pseudostationary segments of voiced sounds". Informatica, 7(4), 469–484, 1996. .
  15. Jelinek F. , "Statistical Methods to Speech Recognition", MIT Press, 1999.
  16. Antanas Lipeik, et. al, " Development of isolted word speech recognition system", Informatica, Vol13, NO. 1, pp. 37-46, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Speech Recognition Isolated Word Uncertainty Vector Quantization Euclidean Distance