Emotion recognition using IG-based feature compensation and continuous support vector machines
Chung-Hsien Wu,
Ze-Jing Chuang
Abstract:This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach is proposed to characterize the feature space with better discriminability among emotional states. The compensation vec… Show more
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