A kind of fuzzy-neural networks for text-independent speaker identification

A novel approach to establish membership functions of fuzzy states based on a functional-link neural network (FLNN) for text-independent speaker identification is proposed. Parameters in corresponding states derived from training utterances uttered by each speaker are fed to a fuzzy statistical model to form the histograms of the states. Data in normalized histograms are used as learning samples for the FLNNs. A set of FLNNs is used as a speaker's model. For each unknown speaker's voice in the test, parameters are fed to every FLNN corresponding to each fuzzy state respectively, the FLNN generalizes the degree of membership to the state, then the system scores the degree of membership to the speaker's model and chooses the speaker whose model's degree of membership is the highest.