Behavior of a Bayesian adaptation method for incremental enrollment in speaker verification

Classical adaptation approaches are generally used for speaker or environment adaptation of speech recognition systems. In this paper, we use such techniques for the incremental training of client models in a speaker verification system. The initial model is trained on a very limited amount of data and then progressively updated with access data, using a segmental-EM procedure. In supervised mode (i.e. when access utterances are certified), the incremental approach yields equivalent performance to the batch one. We also investigate on the impact of various scenarios of impostor attacks during the incremental enrollment phase. All results are obtained with the Picassoft platform - the state-of-the-art speaker verification system developed in the PICASSO project.


Published in:
ICASSP2000 - IEEE International Conference on Acoustics, Speech, and Signal Processing
Presented at:
ICASSP2000 - IEEE International Conference on Acoustics, Speech, and Signal Processing
Year:
2000
Publisher:
Istanbul, Turkey
Keywords:
Note:
IDIAP-RR 00-02
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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