Recognition Of Emotion Using Reconstructed Phase Space Of Speech

Abstract

In recent years, automatic recognition of human’s emotion from speech has become one of the most important research areas, which can improve man-machine interaction. In this study, we proposed new features derived from reconstructed phase space (RPS) of speech. To this end, the RPS is uniformly divided into non-overlapping discrete cells and the number of points included in each cell is counted to form the proposed feature vector. Then multiple classifiers were examined to classify speech samples according to their emotional states. Our experimental results have demonstrated the potential and promise of proposed RPS based features as a useful combination for standard prosodic and spectral features. The best average recognition rate of 89.34% was obtained for classifying seven emotion categories in the Berlin database using a support vector machine with both radial basis function and polynomial kernels.