Speaker recognition and the acoustic speech space.
Robert Stapert, John S. D. Mason · 2001
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 recog-nition results. Comparisons with vector quantisation (V Q) indicate that sensible use of out-of-class data is essential for optimising a recognition system. 1.