An extension of the well-known probabilistic neural network (PNN), to generalized locally recurrent PNN (GLRPNN) is introduced. This extension renders GLRPNN, in contrast to PNN, sensitive to the context, in which events occur. A GLRPNN is therefore, able to identify time or spatial correlations. This capability can be exploited to improve performance on classification tasks. A fast three-step algorithm for training GLRPNN is also proposed. The first two steps are identical to the training of traditional PNN, while the third step exploits the differential evolution optimization method. The performance of the proposed methodology on the task of text-independent speaker verification is contrasted with that of locally recurrent PNN, diagonal recurrent neural networks, infinite impulse response and finite impulse response MLP-based structures, as well as with a Gaussian mixture models-based classifier.
['Todor Ganchev', 'Nikos Fakotakis', 'Dimitris K. Tasoulis', 'Michael N. Vrahatis']
Generalized locally recurrent probabilistic neural networks for text-independent speaker verification