ISCA Archive Odyssey 2001
ISCA Archive Odyssey 2001

Speaker recognition and the acoustic speech space

Robert Stapert, John S. Mason

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 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 indicate that sensible use of out-of-class data is essential for optimising a recognition system.


Cite as: Stapert, R., Mason, J.S. (2001) Speaker recognition and the acoustic speech space. Proc. The Speaker and Language Recognition Workshop (Odyssey 2001), 195-199

@inproceedings{stapert01_odyssey,
  author={Robert Stapert and John S. Mason},
  title={{Speaker recognition and the acoustic speech space}},
  year=2001,
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2001)},
  pages={195--199}
}