Speech Emotion Classification on a Riemannian Manifold

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.