Robust methods of updating model and a priori threshold in speaker verification

Authors:
T. Matsui;T. Nishitani;S. Firui
Affiliations:
NTT Human Interface Labs., Tokyo, Japan;-;-
Venue:
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Year:
1996

Citing 0
Cited 3

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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.