Speaker verification with a priori threshold determination using kernel-based probabilistic neural networks

This paper compares kernel-based probabilistic neural networks for speaker verification. Experimental evaluations based on 138 speakers of the YOHO corpus using probabilistic decision-based neural networks (PDBNNs), Gaussian mixture models (GMMs) and elliptical basis function networks (EBFNs) as speaker models were conducted. The original PDBNN training algorithm was also modified to make PDBNNs appropriate for speaker verification. Results show that the equal error rate obtained by PDBNNs and GMMs is about half of that of EBFNs (1.19% vs. 2.73%), suggesting that GMM- and PDBNN-based speaker models outperform the EBFN one. This work also finds that the globally supervised learning of PDBNNs is able to find a set of decision thresholds that reduce the variation in FAR, whereas the ad hoc approach used by the EBFNs and GMMs is not able to do so. This property makes the performance of PDBNN-based systems more predictable.