Mel scaled M-band wavelet filter bank for speech recognition

Abstract

A Mel scaled M-band wavelet filter bank structure is used to extract the robust acoustic feature for speech recognition application. The proposed filter bank can provide flexibility of frequency partition that decomposes the speech signal into the M-frequency band. To estimate the difference between Mel scaled M-band wavelet and dyadic wavelet filter bank, relative bandwidth deviation (RBD) and root mean square bandwidth deviation (RMSBD) with respect to baseline (Mel filter bank bandwidth) is calculated. Proposed filter bank gives 40.90 and 49.84% reduction for RBD and RMSBD respectively, over 24-dyadic wavelet filter bank. Feature extraction from the proposed filter bank using AMUAV corpus shows an improvement in terms of word recognition accuracy (WRA) at all SNR range (20 dB to 0 dB) over baseline (MFCC) features. For AMUAV corpus, the proposed feature shows the maximum improvement in WRA of 3.93% over baseline features and 3.90% over dyadic wavelet filter bank features. When applied to the VidTIMIT corpus, proposed features show the maximum improvement in WRA of 1.64% over baseline features and 4.43% over dyadic features.