Overlapped sub-band modulation spectrum normalization techniques for robust speech recognition

Abstract

This paper proposes a novel approach to enhance the speech features in noise robustness for speech recognition. In the proposed approach, the speech feature time sequence is first converted into the modulation spectral domain via discrete Fourier transform (DFT). The magnitude part of the modulation spectrum is decomposed into overlapped non-uniform sub-band segments, and then each sub-band segment is individually processed by a specific statistics normalization method, like mean and variance normalization (MVN) and histogram equalization (HEQ). Finally, we reconstruct the feature time sequence with all the modified sub-band magnitude spectral segments together with the original phase spectrum using the inverse DFT. During the process, the components that correspond to more important modulation spectral bands in the feature sequence can be processed separately, and more spectral samples within each band give rise to more accurate statistic estimates due to overlapping the adjacent segments. For the Aurora-2 clean-condition training task, the new proposed method gives rise to significant improvement in recognition accuracy over the baseline results, and it behaves better than the similar technique dealing with non-overlapped sub-bands.