11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Multi-Class and Hierarchical SVMs for Emotion Recognition

Ali Hassan, Robert I. Damper

University of Southampton, UK

This paper extends binary support vector machines to multiclass classification for recognising emotions from speech. We apply two standard schemes (one-versus-one and one-versus rest) and two schemes that form a hierarchy of classifiers each making a distinct binary decision about class membership, on three publicly-available databases. Using the OpenEAR toolkit to extract more than 6000 features per speech sample, we have been able to outperform the state-of-the-art classification methods on all three databases.

Full Paper

Bibliographic reference.  Hassan, Ali / Damper, Robert I. (2010): "Multi-class and hierarchical SVMs for emotion recognition", In INTERSPEECH-2010, 2354-2357.