Designing a speaker-discriminative adaptive filter bank for speaker recognition

Abstract

A new filter bank approach for speaker recognition front-end is proposed. The conventional mel-scaled filter bank is replaced with a speaker-discriminative filter bank. Filter bank is selected from a library in adaptive basis, based on the broad phoneme class of the input frame. Each phoneme class is associated with its own filter bank. Each filter bank is designed in a way that emphasizes discriminative subbands that are characteristic for that phoneme. Experiments on TIMIT corpus show that the proposed method outperforms traditional MFCC features.