Probabilistic neural networks combined with GMMs for speaker recognition over telephone channels

We study the applicability of probabilistic neural networks (PNNs) as core classifiers to medium scale speaker recognition over fixed telephone networks. In particular, banking applications with up to 400 enrolled speakers and short training times are targeted. Two PNN-based open-set text-independent systems, for speaker identification and speaker verification, respectively, are presented. The performance of these systems is studied with and without the use of a supporting Gaussian mixture models classifier. Results from experiments carried out on the Polycost and SpeechDat(II)-Greek corpus, with training times as short as 43 seconds, are reported.