Kernel-Based Probabilistic Neural Networks with Integrated Scoring Normalization for Speaker Verification

Authors:
Kwok-Kwong Yiu;Man-Wai Mak;Sun-Yuan Kung
Affiliations:
-;-;-
Venue:
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2002

Citing 3
Cited 0

A two-stage scoring method combining world and cohort models for speaker verification

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

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Abstract

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.