A wavelet and filter bank framework for phonetic classification

We present a wavelet and filter bank framework for context-independent phonetic classification with the aim of extending the work towards a full speech recognition system. The framework addresses the limitations of the Fourier analysis stage commonly used for short-time spectral representation of speech signals. Also, previous research pertaining to wavelet analysis for speech processing mostly makes use of off-the-shelf wavelets and dyadic-based signal decomposition. Our framework provides more flexibility by taking advantage of the relationship between wavelet transforms and filter banks, and using two filter design techniques as well as 'rational' wavelets. On the standard 39 phone TIMIT classification task, we achieve 22.9% error rate on the core test set using rational filter banks and 4-fold aggregation. This is improved to 18.5% when combined with multiple classifiers defined over non-wavelet acoustic measurements.