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 [BibTeX] [Marc21]
Behavior of a Bayesian adaptation method for incremental enrollment in speaker verification
Type of publication: Idiap-RR
Citation: Adapt-ICASSP2000-IRR2000
Number: Idiap-RR-02-2000
Year: 2000
Institution: IDIAP
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.
Userfields: ipdmembership={speech},
Projects Idiap
Authors Fredouille, Corinne
Mariéthoz, Johnny
Jaboulet, Cédric
Hennebert, Jean
Mokbel, Chafic
Bimbot, Frédéric
Crossref by Adapt-ICASSP2000
Added by: [UNK]
Total mark: 0
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