Challenges and Research Directions for Adaptive Biometric Recognition Systems
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Robust speaker recognition against utterance variations
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
A GMM-based robust incremental adaptation with a forgetting factor for speaker verification
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Pertinent Prosodic Features for Speaker Identification by Voice
International Journal of Mobile Computing and Multimedia Communications
VoCMex: a voice corpus in Mexican Spanish for research in speaker recognition
International Journal of Speech Technology
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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.