Adding dimensional features for emotion recognition on speech

Abstract

Developing accurate emotion recognition systems requires extracting suitable features of these emotions. In this paper, we propose an original approach of parameters extraction based on the strong, theoretical and empirical, correlation between the emotion categories and the dimensional emotions parameters. More precisely, acoustic features and dimensional emotion parameters are combined for better speech emotion characterisation. The procedure consists in developing arousal and valence models by regression on the training data and estimating, by classification, their values in the test data. Hence, when classifying an unknown sample into emotion categories, these estimations could be integrated into the feature vectors. It is noted that the results using this new set of parameters show a significant improvement of the speech emotion recognition performance.