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DOI码：10.1109/ACII.2009.5349587

发表刊物：Proceedings of the 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009)

摘要：This paper deals with speech emotion analysis within the context of increasing awareness of the wide application potential of affective computing. Unlike most 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 the valence dimension and a multistage classification scheme driven by a dimensional emotion model for better emotional class discrimination. Experimented on the Berlin dataset 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 the DES dataset with five emotion states, our approach achieves an 81% recognition rate when the best performance in the literature to our knowledge is 76.15% on the same dataset.

合写作者：Dellandrea, E., Liming Chen, Weibei Dou,Dou, Weibei

第一作者：Zhongzhe Xiao,Hou, Huan, Dou, Weibei

论文类型：会议论文

通讯作者：Dou, Weibei

页面范围：8 pp.

是否译文：否

发表时间：2009-09-10