Stress and emotion recognition using log-Gabor filter analysis of speech spectrograms

We present new methods that extract characteristic features from speech magnitude spectrograms. Two of the presented approaches have been found particularly efficient in the process of automatic stress and emotion classification. In the first approach, the spectrograms are sub-divided into ERB frequency bands and the average energy for each band is calculated. In the second approach, the spectrograms are passed through a bank of 12 log-Gabor filters and the outputs are averaged and passed through an optimal feature selection procedure based on mutual information criteria. The proposed methods were tested using single vowels, words and sentences from SUSAS data base with 3 classes of stress, and spontaneous speech recordings made by psychologists (ORI) with 5 emotional classes. The classification results based on the Gaussian mixture model show correct classification rates of 40%-81%, for different SUSAS data sets and 40%-53.4% for the ORI data base.