The study of q-logarithmic modulation spectral normalization for robust speech recognition

This paper presents a novel use of the generalized logarithm operation (q-logarithm) in refining the modulation spectrum of speech features for noise-robust speech recognition. The resulting new method, generalized logarithmic modulation spectral mean normalization (GLMSMN), equalizes the average of the magnitude modulation spectrum in q-logarithmic domain for different utterances in order to alleviate the effect of noise. In the Aurora-2 connected-digit database and evaluation task, the presented GLMSMN operating on the MVN features reveals significant improvement in recognition accuracy in comparison with the MFCC baseline and MVN. The overall averaged recognition accuracy brought by GLMSMN can be nearly 90%.