Improving automatic emotion recognition from speech signals

We present a speech signal driven emotion recognition sys-tem. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes clas-sifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spec-tral and HMM-based features for the evaluation of emotion recognition with Gaussian mixture model (GMM) based clas-sifiers. Spectral features consist of mel-scale cepstral coeffi-cients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of mean normalized values of pitch, first derivative of pitch and inten-sity. Unsupervised training of HMM structures are employed to define prosody related temporal features for the emotion recog-nition problem. We also investigate data fusion of different fea-tures and decision fusion of different classifiers, which are not well studied for emotion recognition framework. Experimen-tal results of automatic emotion recognition with the INTER-SPEECH 2009 Emotion Challenge corpus are presented. Index Terms: emotion recognition, prosody modeling 1.