Speech Recognition by Wavelet Analysis
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10.5120/1968-2635 |
Nitin Trivedi, Dr. Vikesh Kumar, Saurabh Singh, Sachin Ahuja and Raman Chadha. Article: Speech Recognition by Wavelet Analysis. International Journal of Computer Applications 15(8):27–32, February 2011. Full text available. BibTeX
@article{key:article,
author = {Nitin Trivedi and Dr. Vikesh Kumar and Saurabh Singh and Sachin Ahuja and Raman Chadha},
title = {Article: Speech Recognition by Wavelet Analysis},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {15},
number = {8},
pages = {27--32},
month = {February},
note = {Full text available}
}
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.
The major issues concerning the design of this Wavelet based speech recognition system are choosing optimal wavelets for speech signals, decomposition level in the DWT, selecting the feature vectors from the wavelet coefficients. More specifically automatic classification of various speech signals using the DWT is described and compared using different wavelets. Finally, wavelet based feature extraction system and its performance on an isolated word recognition problem are investigated. For the classification of the words, three layered feed forward network is used.
Reference
- 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.
- 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.
- 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.
- M. Misiti, Y. Misiti, G. Oppenheim and J. Poggi, Matlab Wavelet Tool Box, The Math Works Inc.,2000 Page: 795.
- George Tzanetakis, Georg Essl, Perry Cook, “Audio Analysis using the Discrete Wavelet Transform” Organized sound, Vol. 4(3), 2000.
- 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.
- X. Huang, “Speaker normalization for speech recognition”, IEEE International Conference on Acoustics, Speech, and Signal Processing, 1:465-468, March 1992.
- S. Tamura, A Waibel, “Noise reduction using connectionist models.” IEEE International Conference on Acoustics, Speech, and Signal Processing, 1:553-556, April 1988.
- S. Young, “A review of large vocabulary continues-speech recognition,” Proc. IEEE Sig. Processing. Mag. (September) (1996) 45-57.
- 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.