Elliptical basis function networks and radial basis function networks for speaker verification: a comparative study

摘要

It is well known that radial basis function (RBF) networks require a large number of function centers if the data to be modeled contain clusters with complicated shape. This paper proposes to overcome this problem by incorporating full covariance matrices into the RBF structure and to use the expectation-maximization (EM) algorithm to estimate the network parameters. The resulting networks, referred to as the elliptical basis function (EBF) networks, are applied to text-independent speaker verification. Experimental evaluations based on 258 speakers of the TIMIT corpus show that smaller size EBF networks with basis function parameters determined by the EM algorithm outperform the large RBF networks trained by the conventional approach.