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DOI码：10.1007/s11042-009-0319-3

发表刊物：MULTIMEDIA TOOLS AND APPLICATIONS

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 multi-stage 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, Emmanuel, Dou, Weibei, Chen, Liming

第一作者：Xiao, Zhongzhe,Dou, Weibei,测试老师, Shuai, Constans, Jean-Marc

论文类型：期刊论文

卷号：46

期号：1

页面范围：119-145

ISSN号：1380-7501

是否译文：否

发表时间：2010-01-01