Robust methods of updating model and a priori threshold in speaker verification
- 24 December 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 97-100
- https://doi.org/10.1109/icassp.1996.540299
Abstract
We describe a method of updating a hidden Markov model (HMM) for speaker verification using a small amount of new data for each speaker. The HMM is updated by adapting the model parameters to the new data by maximum a posteriori (MAP) estimation. The initial values of the a priori parameters in MAP estimation are set using training speech used for first creating a speaker HMM. We also present a method of resetting the a priori threshold as the updating of the model proceeds. Evaluation of the performance of the two methods using 10 male speakers showed that the verification error rate was about 42% of that without updating.Keywords
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