Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18839
Title: A comparative study on kernel-based probabilistic neural networks for speaker verification
Authors: Yiu, KK
Mak, MW 
Kung, SY
Issue Date: 2002
Source: International journal of neural systems, 2002, v. 12, no. 5, p. 381-397 How to cite?
Journal: International journal of neural systems 
Abstract: This paper compares kernel-based probabilistic neural networks for speaker verification based on 138 speakers of the YOHO corpus. Experimental evaluations using probabilistic decision-based neural networks (PDBNNs), Gaussian mixture models (GMMs) and elliptical basis function networks (EBFNs) as speaker models were conducted. The original training algorithm of PDBNNs was also modified to make PDBNNs appropriate for speaker verification. Results show that the equal error rate obtained by PDBNNs and GMMs is less than that of EBFNs (0.33% vs. 0.48%), suggesting that GMM- and PDBNN-based speaker models outperform the EBFN ones. This work also finds that the globally supervised learning of PDBNNs is able to find decision thresholds that not only maintain the false acceptance rates to a low level but also reduce their variation, whereas the ad-hoc threshold-determination approach used by the EBFNs and GMMs causes a large variation in the error rates. This property makes the performance of PDBNN-based systems more predictable.
URI: http://hdl.handle.net/10397/18839
ISSN: 0129-0657
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