Abstract
Speaker authentication has been developed rapidly in the last few decades. This research work attempts to extract the hidden features of human voice that is able to simulate human auditory system characteristics in speaker authentication. The hidden features are then presented as inputs to a Multi-Layer Perceptron Neural Network and Generic Self-organizing Fuzzy Neural Network to verify the speakers with high accuracy. Based on the experimental results, the two networks are able to verify speakers using two method in extracting hidden features from the recorded voice sources.
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