12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Denoising Using Optimized Wavelet Filtering for Automatic Speech Recognition

Randy Gomez, Tatsuya Kawahara

Kyoto University, Japan

We present an improved denoising method based on filtering of the noisy wavelet coefficients using a Wiener gain for automatic speech recognition (ASR). We optimize the wavelet parameters for speech and different noise profiles to achieve a better estimate of the Wiener gain for effective filtering. Moreover, we introduce a scaling parameter in the Wiener gain to minimize mismatch caused by distortion during the denoising process. Experimental results in large vocabulary continuous speech recognition (LVCSR) show that the proposed method is effective and robust to different noise conditions.

Full Paper

Bibliographic reference.  Gomez, Randy / Kawahara, Tatsuya (2011): "Denoising using optimized wavelet filtering for automatic speech recognition", In INTERSPEECH-2011, 1673-1676.