IG-Based feature extraction and compensation for emotion recognition from speech

Authors:
Ze-Jing Chuang;Chung-Hsien Wu
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 2
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Abstract

This paper presents an approach to 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 vector with respect to each emotional state is estimated using the Minimum Classification Error (MCE) algorithm. The IG-based feature vectors compensated by the compensation vectors are used to train the Gaussian Mixture Models (GMMs) for each emotional state. The emotional state with the GMM having the maximal likelihood ratio is determined as the final output. The experimental result shows that IG-based feature extraction and compensation can obtain encouraging performance for emotion recognition.