Full Covariance Matrices based Gaussian Mixture Models



Universal Background Models combined with Gaussian Mixture Models (UBM - GMM) is a common approach to speaker verifcation systems. In general, we use diagonal covariance matrices. This simplification allows us to have faster training steps during speaker recognition. We will explore the case of full-covariance along with the additional complexity and the benefits in terms of speaker recognition performances. All experiments will be performed on NIST-SRE 2010 datasets.