Cepstral domain segmental feature vector normalization for noise robust speech recognition
Speech Communication - Special issue on robust speech recognition
MVA Processing of Speech Features
IEEE Transactions on Audio, Speech, and Language Processing
Quantile based histogram equalization for noise robust large vocabulary speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Normalization of the Speech Modulation Spectra for Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we present two novel algorithms to improve the noise robustness of features in speech recognition: modulation spectrum replacement (MSR) and modulation spectrum filtering (MSF). The magnitude spectra of feature streams are updated by referring to the information collected in the clean training set, and the resulting new feature streams are more noise-robust to achieve higher recognition accuracy. In experiments conducted on the Aurora-2 noisy digit database, we show that the proposed MSR achieves an average relative error reduction rate of nearly 57% compared to baseline processing, and MSF is specifically effective in enhancing the features preprocessed by conventional feature normalization methods to achieve even better recognition accuracy in noise-corrupted situations.