RBF neural networks and MTI for text independent speaker identification

Artificial neural networks applied to speaker recognition tasks have being addressed by several researchers. This paper presents an investigation of the use of radial basis function (RBF) neural networks as classifiers applied to speaker identification tasks. A novel way to organize the speech frames in order to represent the speakers-the minimal temporal information (MTI)-is introduced and a comparison with the traditional multilayer perceptron (MLP) is presented. The results obtained indicate that the use of RBF neural networks are promising in speaker recognition and the MTI strategy to organize the speech frames are able to improve the RBF recognition rate.