A Novel Classifier Based on Enhanced Lipschitz Embedding for Speech Emotion Recognition

Authors:
Mingyu You;Guo-Zheng Li;Luonan Chen;Jianhua Tao
Affiliations:
School of Computer Engineering and Science, Shanghai University, Shanghai, China 200072;School of Computer Engineering and Science, Shanghai University, Shanghai, China 200072 and Institute of System Biology, Shanghai University, Shanghai, China 200444;Institute of System Biology, Shanghai University, Shanghai, China 200444;National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, China 100080
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

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Abstract

The paper proposes a novel classifier named ELEC (Enhanced Lipschitz Embedding based Classifier) in the speech emotion recognition system. ELEC adopts geodesic distance to preserve the intrinsic geometry of speech corpus and embeds the high dimensional feature vector into a lower space. Through analyzing the class labels of the neighbor training vectors in the compressed space, ELEC classifies the test data into six archetypal emotional states, i.e. neutral, anger, fear, happiness, sadness and surprise. Experimental results on a benchmark data set demonstrate that compared with the traditional methods, the proposed classifier of ELEC achieves 17% improvement in average for speaker-independent emotion recognition and 13% for speaker-dependent.