Speech Recognition by Wavelet Analysis

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. General Terms

[1]  Xuedong Huang.  Speaker normalization for speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  George Tzanetakis,et al.  Audio Analysis using the Discrete Wavelet Transform , 2001 .

[3]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Alex Waibel,et al.  Noise reduction using connectionist models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[5]  Chin-Hui Lee,et al.  Iterative noise and channel estimation under the stochastic matching algorithm framework , 1997, IEEE Signal Processing Letters.

[6]  Gérard Chollet,et al.  Robust speech parameters extraction for word recognition in noise using neural networks , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[7]  Steve Young,et al.  A review of large-vocabulary continuous-speech recognition , 1996 .

[8]  Minyue Fu,et al.  The use of wavelet transforms in phoneme recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[9]  G..  A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[10]  N. Deshmukh,et al.  Hierarchical search for large-vocabulary conversational speech recognition: working toward a solution to the decoding problem , 1999 .