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.
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Ye, C., Liu, J., Chen, C., Song, M., Bu, J. (2008). Speech Emotion Classification on a Riemannian Manifold. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_7
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DOI: https://doi.org/10.1007/978-3-540-89796-5_7
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