| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 97 === The environmental mismatch caused by additive noise and/or channel distortion often degrades the performance of a speech recognition system seriously. Various robustness techniques have been proposed to reduce this mismatch, and one category of them aims to normalize the statistics of speech features in both training and testing conditions. In general, these statistics normalization methods deal with the speech feature sequences in a full-band manner, which somewhat ignores the fact that different modulation frequency components have unequal importance for speech recognition.
With the above observations, in this paper we propose that the speech feature streams be processed in a sub-band manner. 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 20.32% and 16.39% over the conventional MVN and HEQ, respectively. These results reveal that the proposed methods significantly enhance the robustness of speech features in noise-corrupted environments.
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