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Sub-band feature statistics compensation techniques based on discrete wavelet transform for robust speech recognition

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2 Author(s)
Hao-Teng Fan ; Dept of Electr. Eng., Nat. Chi Nan Univ., Taiwan ; Jeih-weih Hung

This paper proposes a novel scheme in performing feature statistics normalization techniques for robust speech recognition. In the proposed approach, the processed temporal-domain feature sequence is first decomposed into non-uniform sub-bands using discrete wavelet transform (DWT), and then each sub-band stream is individually processed by the well-known normalization methods, like mean and variance normalization (MVN) and histogram equalization (HEQ). Finally, we reconstruct the feature stream with all the modified sub-band streams using inverse DWT. With this process, the components that correspond to more important modulation spectral bands in the feature sequence can be processed separately. For the Aurora-2 clean-condition training task, the new proposed sub-band MVN and HEQ provide relative error rate reductions of 20.18% and 19.65% over the conventional MVN and HEQ.

Published in:

Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on

Date of Conference:

June 28 2009-July 3 2009