TY  - RPRT
ID  - fred00:idiap
T1  - Behavior of a Bayesian adaptation method for incremental enrollment in speaker verification
A1  - Fredouille, Corinne
A1  - Mariethoz, Johnny
A1  - Jaboulet, Cédric
A1  - Hennebert, Jean
A1  - Mokbel, Chafik
A1  - Bimbot, Frédéric
Y1  - 2000
IS  - 02
T2  - IDIAP
UR  - http://diuf.unifr.ch/people/henneber/cv/download/inc_enr_idiap_RR_2000.pdf
KW  - Biometrics
KW  - Pattern Recognition
KW  - Speaker Verification
N2  - 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.
ER  -