Emotional Speech Analysis on Nonlinear Manifold

Authors:
Mingyu You;Chun Chen;Jiajun Bu;Jia Liu;Jianhua Tao
Affiliations:
ZheJiang University, Hangzhou, P.R.CHINA;ZheJiang University, Hangzhou, P.R.CHINA;ZheJiang University, Hangzhou, P.R.CHINA;ZheJiang University, Hangzhou, P.R.CHINA;Chinese Academy of Sciences, Beijing, CHINA
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

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Cited 4

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

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Survey on speech emotion recognition: Features, classification schemes, and databases

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Dimensionality reduction-based spoken emotion recognition

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Abstract

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.