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Behavior of a Bayesian adaptation method for incremental enrollment in speaker verification
Type of publication: Techreport
Citation: fred00:idiap
Number: 02
Year: 2000
Institution: IDIAP
URL: http://diuf.unifr.ch/people/he...
Abstract: 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.
Keywords: Biometrics, Pattern Recognition, Speaker Verification
Authors Fredouille, Corinne
Mariethoz, Johnny
Jaboulet, Cédric
Hennebert, Jean
Mokbel, Chafik
Bimbot, Frédéric
Added by: []
Total mark: 0
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