Progressive Memory-Based Parametric Non-Linear Feature Equalization

This paper analyzes the benefits and drawbacks of PEQ (Parametric Non-linear Equalization), a features normalization technique based on the parametric equalization of the MFCC parameters to match a reference probability distribution. Two limitations have been outlined: the distortion intrinsic to the normalization process and the lack of accuracy in estimating normalization statistics on short sentences. Two evolutions of PEQ are presented as solutions to the limitations encountered. The effects of the proposed evolutions are evaluated on three speech corpora, namely WSJ0, AURORA-3 and HIWIRE cockpit databases, with different mismatch conditions given by convolutional and/or additive noise and non-native speakers. The obtained results show that the encountered limitations can be overcome by the newly introduced techniques.