Text-independent speaker identification using a hybrid neural network and conformity approach

A hybrid neural network consisting of a Kohonen map and a multilayer perceptron is described. This hybrid network is designed for the text-independent speaker identification. Each speaker known to the system has a personalized hybrid neural network. In the hybrid network the multilayer perceptron works in a histogram feature space created by the Kohonen map. The map is partitioned into a set of overlapping fields. The modified field histograms are obtained and they are directly fed to the hidden layer neurons. To obtain the histograms the input vectors in supra-segments are used. The hybrid system is evaluated with a 10 speakers database of telephone speech. The experimental results have shown the validity of this kind of cooperation between a Kohonen map and a multilayer perceptron