Maximum likelihood estimation of elliptical basis function parameters with application to speaker verification

Abstract

The use of the K-means algorithm and the K-nearest neighbor heuristic in estimating the radial basis function (RBF) parameters may produce sub-optimal performance when the input vectors contain correlated components. 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 elliptical basis function (EBF) networks, are applied to text-independent speaker verification. To examine the robustness of the networks in a noisy environment, both clean speech and telephone speech have been used. Experimental results show that smaller size EBF networks with basis function parameters determined by the EM algorithm outperform the large RBF networks trained in the conventional approach. The best error rates achieved by the EBF networks is 3.70%, while that achieved by the RBF networks is 10.37%.