Wavelet based feature extraction for phoneme recognition

In an effort to provide a more efficient representation of the acoustical speech signal in the pre-classification stage of a speech recognition system, we consider the application of the Best-Basis Algorithm of Coifman and Wickerhauser. This combines the advantages of using a smooth, compactly-supported wavelet basis with an adaptive time-scale analysis dependent on the problem at hand. We start by briefly reviewing areas within speech recognition where the Wavelet Transform has been applied with some success. Examples include pitch detection, formant tracking, phoneme classification. Finally, our wavelet based feature extraction system is described and its performance on a simple phonetic classification problem given.