Kernel-based probabilistic neural networks with integrated scoring normalization for speaker verification

Kwok Kwong Yiu, Man Wai Mak, Sun Yuan Kung

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


This paper investigates kernel-based probabilistic neural networks for speaker verification in clean and noisy environments. In particular, it compares the performance and characteristics of speaker verification systems that use probabilistic decision-based neural networks (PDBNNs), Gaussian mixture models (GMMs) and elliptical basis function networks (EBFNs) as speaker models. Experimental evaluations based on 138 speakers of the YOHO corpus and its noisy variants were conducted. The original PDBNN training algorithm was also modified to make PDBNNs appropriate for speaker verification. Experimental evaluations, based on 138 speakers and the visualization of decision boundaries, indicate that GMM- and PDBNN-based speaker models are superior to the EBFN ones in terms of performance and generalization capability. This work also finds that PDBNNs and GMMs are more robust than EBFNs in verifying speakers in noise environments.
Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540002626, 9783540002628
Publication statusPublished - 1 Jan 2002
Event3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002 - Hsinchu, Taiwan
Duration: 16 Dec 200218 Dec 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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