Extended Powered Cepstral Normalization (P-CN) with Range Equalization for Robust Features in Speech Recognition

Cepstral normalization has been popularly used as a powerful approach to produce robust features for speech recognition. A new approach of Powered Cepstral Normalization (P-CN) was recently proposed to normalize the MFCC parameters in the r(1)-th order powered domain, where r(1) > 1.0, and then transform the features back by an 1/r(2) power order to a better recognition domain, and it was shown to produce robust features. Here we further extend P-CN to a more effective and efficient form, in which we can on-line find good values of r(2) for each utterance in real time based on the concept of dynamic range equalization. The basic idea is that the difference in dynamic ranges of feature parameters is in fact a good indicator for the mismatch degrading the recognition performance. Extensive experimental results showed that the Extended P-CN with range equalization proposed in this paper significantly outperforms the conventional Cepstral Normalization and P-CN in all noisy conditions.