Covariance clustering on Riemannian manifolds for acoustic model compression

A new method of covariance clustering for acoustic model compression is proposed. Since covariance matrices do not form a Euclidean vector space, standard vector clustering algorithms cannot be used effectively for covariance clustering. In this paper, we propose a novel clustering algorithm based on a Riemannian framework, where the covariance space is considered as a Riemannian manifold equipped with the Fisher information metric, and notions of distance and mean are defined on the manifold. The LBG clustering algorithm is naturally extended to the covariance space under the Riemannian framework. Experimental results show the effectiveness of the proposed method, reducing the acoustic model size nearly to the half without noticeable loss in recognition performance.