Speech Emotion Classification on a Riemannian Manifold

Authors:
Chengxi Ye;Jia Liu;Chun Chen;Mingli Song;Jiajun Bu
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, P. R. China 310027;College of Computer Science, Zhejiang University, Hangzhou, P. R. China 310027;College of Computer Science, Zhejiang University, Hangzhou, P. R. China 310027;College of Computer Science, Zhejiang University, Hangzhou, P. R. China 310027;College of Computer Science, Zhejiang University, Hangzhou, P. R. China 310027
Venue:
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2008

Citing 6
Cited 1

Speech recognition by machines and humans

Speech Communication
A Riemannian Framework for Tensor Computing

International Journal of Computer Vision
Covariance Tracking using Model Update Based on Lie Algebra

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Hidden Markov model-based speech emotion recognition

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Riemannian geometry for the statistical analysis of diffusion tensor data

Signal Processing
Region covariance: a fast descriptor for detection and classification

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II

Visualization of Riemannian-manifold-valued elements by multidimensional scaling

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel algorithm for speech emotion classification. In contrast to previous methods, we additionally consider the relations between simple features by incorporating covariance matrices as the new feature descriptors. Since non-singular covariance matrices do not lie on a linear space, we endow the space with an affine invariance metric and render it into a Riemannian manifold. After that we use the tangent space to approximate the manifold. Classification is performed in the tangent space and a generalized principal component analysis is presented. We test the algorithm on speech emotion classification and the experiment results show an improvement at around 13%(+3% with PCA) in recognition accuracy. Based on that we are able to train one simple model to accurately differentiate the emotions from both genders.