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Optimizing Wavelet Parameters for Dereverberation in Automatic Speech Recognition

By Randy Gomez and Tatsuya Kawahara


We present an optimization method of the wavelet parameters for dereverberation in automatic speech recognition (ASR). By tuning the wavelet parameters to improve the acoustic model likelihood, wavelet-based dereverberation methods become more effective in the ASR application. We evaluate several existing wavelet-based methods and optimize them, based on our proposed scheme. Experimental evaluations through ASR experiments demonstrate significant improvement for all methods with the proposed optimization

Topics: Index Terms, Robustness, Speech recognition, Dereverberation
Year: 2010
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