Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31457
Title: Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification
Authors: Mak, MW 
Kung, SY
Issue Date: 2000
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks, 2000, v. 11, no. 4, p. 961-969 How to cite?
Journal: IEEE transactions on neural networks 
Abstract: This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the expectation-maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker verification experiments involving 258 speakers from a phonetically balanced, continuous speech corpus (TIMIT). We propose a verification procedure using RBF and EBF networks as speaker models and show that the networks are readily applicable to verifying speakers using LP-derived cepstral coefficients as features. Experimental results show that small EBF networks with basis function parameters estimated by the EM algorithm outperform the large RBF networks trained in the conventional approach. The results also show that the equal error rate achieved by the EBF networks is about two-third of that achieved by the vetor quantization (VQ)-based speaker models.
URI: http://hdl.handle.net/10397/31457
ISSN: 1045-9227
DOI: 10.1109/72.857775
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