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Automatic Hierarchical Classification of Emotional Speech

Abstract : Speech emotion is high semantic information and its automatic analysis may have many applications such as smart human-computer interactions or multimedia indexing. As a pattern recognition problem, the feature selection and the structure of the classifier are two important aspects for automatic speech emotion classification. In this paper, we propose a novel feature selection scheme based on the evidence theory. Furthermore, we also present a new automatic approach for constructing a hierarchical classifier, which allows better performance than a global classifier as it is mostly used in the literature. Experimented on the Berlin database, our approach showed its effectiveness, scoring a recognition rate up to 78.64%.
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Submitted on : Tuesday, September 19, 2017 - 10:31:34 AM
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Zhongzhe Xiao, Emmanuel Dellandréa, Weibei Dou, Liming Chen. Automatic Hierarchical Classification of Emotional Speech. 9th International Symposium on Multimedia Workshops, ISMW 2007, Dec 2007, Taichung, Taiwan, Taiwan. pp.291-296, ⟨10.1109/ISM.Workshops.2007.56⟩. ⟨hal-01589817⟩



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