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Reseach Article

Speech Recognition by Wavelet Analysis

by Nitin Trivedi, Dr. Vikesh Kumar, Saurabh Singh, Sachin Ahuja, Raman Chadha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 15 - Number 8
Year of Publication: 2011
Authors: Nitin Trivedi, Dr. Vikesh Kumar, Saurabh Singh, Sachin Ahuja, Raman Chadha
10.5120/1968-2635

Nitin Trivedi, Dr. Vikesh Kumar, Saurabh Singh, Sachin Ahuja, Raman Chadha . Speech Recognition by Wavelet Analysis. International Journal of Computer Applications. 15, 8 ( February 2011), 27-32. DOI=10.5120/1968-2635

@article{ 10.5120/1968-2635,
author = { Nitin Trivedi, Dr. Vikesh Kumar, Saurabh Singh, Sachin Ahuja, Raman Chadha },
title = { Speech Recognition by Wavelet Analysis },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 15 },
number = { 8 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume15/number8/1968-2635/ },
doi = { 10.5120/1968-2635 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:03:41.990391+05:30
%A Nitin Trivedi
%A Dr. Vikesh Kumar
%A Saurabh Singh
%A Sachin Ahuja
%A Raman Chadha
%T Speech Recognition by Wavelet Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 15
%N 8
%P 27-32
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Based on the time‐frequency multi‐resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels.

References
  1. B.T. Tan, M. Fu, A. Spray, P. Dermody, “The use of wavelet transform for phoneme recognition,” Proceedings of the 4th International Conference of Spoken Language Processing Philadelphia, Vol. 4, USA, October 1996, pp.2431-2434.
  2. S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE transactions on Pattern Analysis Machine Intelligence, Vol. 11 1989, pp.674-693.
  3. Oliver Siohan and Chin-Hui Lee “Iterative Noise and Channel Estimation under the Stochastic Matching Algorithm Framework” IEEE Signal Processing, Processing Letters, Vol. 4, No. 11, Nov 1997.
  4. M. Misiti, Y. Misiti, G. Oppenheim and J. Poggi, Matlab Wavelet Tool Box, The Math Works Inc.,2000 Page: 795.
  5. George Tzanetakis, Georg Essl, Perry Cook, “Audio Analysis using the Discrete Wavelet Transform” Organized sound, Vol. 4(3), 2000.
  6. L. Barbier, G. Chollet, “Robust speech parameters extraction for word recognition in noise using neural networks,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Pages: 145-148, May 1991.
  7. X. Huang, “Speaker normalization for speech recognition”, IEEE International Conference on Acoustics, Speech, and Signal Processing, 1:465-468, March 1992.
  8. S. Tamura, A Waibel, “Noise reduction using connectionist models.” IEEE International Conference on Acoustics, Speech, and Signal Processing, 1:553-556, April 1988.
  9. S. Young, “A review of large vocabulary continues-speech recognition,” Proc. IEEE Sig. Processing. Mag. (September) (1996) 45-57.
  10. N. Desmukh, A. Ganapathiraju, J. Picone, “Hierarchical search for large vocabulary conversational speech recognition – working toward a solution to the decoding problem,” IEEE Sig, Process Mag. (September) (1999) 84-107.
Index Terms

Computer Science
Information Sciences

Keywords

Speech recognition feature extraction wavelet transform Discrete Wavelet Transform (DWT)