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

Man Wai Mak, C. K. Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

14 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages6
Publication statusPublished - 1 Dec 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, United States
Duration: 10 Jul 199916 Jul 1999


ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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