Time-frequency feature extraction from spectrograms and wavelet packets with application to automatic stress and emotion classification in speech

Abstract

Three new methods of feature extraction based on time-frequency analysis of speech are presented and compared. In the first approach, speech spectrograms were passed through a bank of 12 log-Gabor filters and the outputs are averaged. In the second approach, the spectrograms were sub-divided into ERB frequency bands and the average energy for each band is calculated. In the third approach, wavelet packet arrays were calculated and passed through a bank of 12 log-Gabor filters and averaged. The feature extraction methods were tested in the process of automatic stress and emotion classification. The feature distributions were modeled and classified using a Gaussian mixture model. The test samp..