Application of voiced-speech variability descriptors to emotion recognition

Abstract

The following paper examines a possibility of applying phone-pronunciation variability descriptors in emotion classification. The proposed group of descriptors comprises a set of statistical parameters of Poincare maps, which are derived for evolution of formant-frequencies and energy of voiced-speech segments. Poincare maps are represented by means of four different parameters that summarize various aspects of plot's scatter. It has been shown that incorporation of the proposed features into a set of commonly-used emotional-speech descriptors, results in a substantial, ten-percent increase in emotion classification performance - recognition rates are at the order of 80% for six-category, speaker independent experiments.