Bhattacharyya distance based emotional dissimilarity measure for emotion classification

Speech is one of the most important signals that can be used to detect human emotions. When speech is modulated by different emotions, spectral distribution of speech is changed accordingly. A Gaussian Mixture Model(GMM) can model the changes in spectral distributions effectively. A GMM-supervector characterizes the spectral distribution of an emotion utterance by the GMM parameters such as the mean vectors and covariance matrices. In this paper, we propose to use the GMM-supervectors that characterize the emotional spectral dissimilarity measure for emotion classification. We employ the GMM-SVM kernel with Bhattacharyya based GMM distance to obtain dissimilarity measure. Beside the first-order statistics of mean, we consider dissimilarity measure using second-order statistics of covariance which describe the shape of the distribution. Experiments are conducted using SVM classifier to classify emotions of anger, happiness, neutral and sadness. We achieve average accuracy of 78.14% for speaker independent emotion classification.