Nonlinear techniques for parameter extraction from quasi-continuous wavelet transform with application to speech

Speaker identification and word spotting will shortly play a key role in a lot of different fields. This paper presents an approach, based on the wavelet transform, to extract features from a speech signal. These features are based on the `modulation model'. An adequate choice of the extracted features dramatically increases the efficiency of the classification performed on the different speakers or on the different words.