Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification

Man Wai Mak, Sun Yuan Kung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

72 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)961-969
Number of pages9
JournalIEEE Transactions on Neural Networks
Volume11
Issue number4
DOIs
Publication statusPublished - 1 Jul 2000

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

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