Speaker recognition and the acoustic speech space
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چکیده
The hypothesis that for a given amount of training data a speaker model has an optimum number of components is examined. This is investigated with regard to Gaussian mixture models (GMM) with and without world model adaptation. Results show that maximising the number of components in a speaker model can improve speaker recognition results. Comparisons with vector quantisation (V Q) indicate that sensible use of out-of-class data is essential for optimising a recognition system.
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تاریخ انتشار 2001