Speech emotion classification using acoustic features

Emotion recognition from speech is a challenging research area with wide applications. In this paper we explore one of the key aspects of building an emotion recognition system: generating suitable feature representation. We extract features from four angles: (1) low-level acoustic features such as intensity, F0, jitter, shimmer and spectral contours etc. and statistical functions over these features, (2) a set of features derived from segmental cepstral-based features scored against emotion-dependent Gaussian mixture models, (3) a set of features derived from a set of low-level acoustic codewords and (4) GMM supervectors constructed by stacking the means or covariance or weights of the adapted mixture components on each utterance. We apply these features for emotion recognition independently and jointly and compare their performance within this task. We build a support vector machine (SVM) classifier based on these features on the IEMOCAP database. The four-class emotion recognition accuracy of 71.9% of our system outperforms the previously reported best results on this dataset.