Locally recurrent probabilistic neural network for text-independent speaker verification

Abstract

This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is proposed. The first two steps are identical to the training of traditional PNNs, while the third step is based on the Differential Evolution optimization method. Finally, the superiority of LRPNNs over PNNs on the task of text-independent speaker verification is demonstrated.