Features selection for primitives estimation on emotional speech

Abstract

This paper examines the importance of different groups of speech acoustic features in the estimation of emotional primitives which define a three-dimensional continuous model of emotions. A set of proposed features is extracted from a database of German spontaneous emotional speech. This features set tries to represent several aspects of emotional content in speech that have been discussed separately in other works. Features selection and dimensionality reduction techniques are applied to find the features subsets that best estimate the emotional primitives Valence, Activation and Dominance. Finally, the emotional primitives are estimated using different classifiers based on the proposed features getting better results than the ones previously reported on related works.