Pattern Recognition, International Conference on (2006)
Aug. 20, 2006 to Aug. 24, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.490
Mingyu You , ZheJiang University, Hangzhou, P.R.CHINA
Chun Chen , ZheJiang University, Hangzhou, P.R.CHINA
Jiajun Bu , ZheJiang University, Hangzhou, P.R.CHINA
Jia Liu , ZheJiang University, Hangzhou, P.R.CHINA
Jianhua Tao , Chinese Academy of Sciences, Beijing, CHINA
This paper presents a speech emotion recognition system on nonlinear manifold. Instead of straight-line distance, geodesic distance was adopted to preserve the intrinsic geometry of speech corpus. Based on geodesic distance estimation, we developed an enhanced Lipschitz embedding to embed the 64-dimensional acoustic features into a sixdimensional space. In this space, speech data with the same emotional state were located close to one plane, which was beneficial to emotion classification. The compressed testing data were classified into six archetypal emotional states (neutral, anger, fear, happiness, sadness and surprise) by a trained linear Support Vector Machine (SVM) system. Experimental results demonstrate that compared with traditional methods of feature extraction on linear manifold and feature selection, the proposed system makes 9%-26% relative improvement in speaker-independent emotion recognition and 5%-20% improvement in speaker-dependent.
C. Chen, J. Liu, J. Bu, J. Tao and M. You, "Emotional Speech Analysis on Nonlinear Manifold," 2006 18th International Conference on Pattern Recognition(ICPR), Hong Kong, 2006, pp. 91-94.