A Dimensional Emotion Model Driven Multi-stage Classification of Emotional Speech

This paper deals with speech emotion analysis within the context of increasing awareness on wide application potential of affective computing. Unlike the most of works in the literature which mainly rely on classical frequency and energy based features along with a single global classifier for emotion recognition, we propose in this paper some new harmonic and Zipf based features for better speech emotion characterization in terms of timbre, rhythm and prosody and a dimensional emotion model driven multi-stage classification scheme for a better emotional class discrimination. Experimented on Berlin dataset [1] with 68 features and six emotion states, our approach shows its effectiveness, displaying a 68.60% classification rate and reaching a 71.52% classification rate when a gender classification is first applied. Using DES dataset having five emotion states, our approach achieves an 81% recognition rate when the best performance in the literature to our knowledge is 66% on the same dataset [2].