VOICE RECOGNITION USING GAUSSIAN MIXTURE MODEL

Speech conveys several levels of information. On a primary level, speech conveys the words or message being spoken, but on a secondary level, speech also reveals information about the speaker. In this article we present an overview of our research efforts in an area-automatic speaker recognition. We base our approach on a statistical speaker-modelling technique that represents the underlying characteristic sounds of a person's voice. Using these models, we build speaker recognizers that are computationally inexpensive and capable of recognizing a speaker regardless of what is being said. Performance of the systems is evaluated for a wide range of speech quality; from clean speech to telephone speech, by using several standard speech corpora. Keywords: MFCC, artificial neural: ann , MandelEllis, perceptron