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

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.