Phonetically trained models for speaker recognition.
In this paper, a speaker recognition system that introduces acoustic information into a Gaussian mixture model (GMM)-based recognizer is presented. This is achieved by using a phonetic classifier during the training phase. The experimental results show that, while maintaining the recognition rate, the decrease in the computational load is between 65% and 80% depending on the number of mixtures of the models.
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