An Implementation of Rational Wavelets and Filter Design for Phonetic Classification

Although wavelet analysis has been proposed for speech processing as an alternative to Fourier analysis, most approaches make use of off-the-shelf wavelets and dyadic tree-structured filter banks. In this paper, we extend previous wavelet-based frameworks in two ways. First, we increase the flexibility in wavelet selection by taking advantage of the relationship between wavelets and filter banks and by designing new wavelets using filter design methods. We adopt two filter design techniques that we refer to as filter matching and attenuation minimization. Second, we improve the flexibility in frequency partitioning by implementing rational as well as dyadic filter banks. Rational filter banks naturally incorporate the critical-band effect in the human auditory system. To test our extensions, we implement an energy-based measurement which we also compare in performance to the mel-frequency cepstral coefficients (MFCCs) in a phonetic classification task. We show that the designed wavelets outperform off-the-shelf wavelets as well as an MFCC baseline