CONF
BenZeghiba_icassp-03/IDIAP
Hybrid HMM/ANN and GMM Combination for User-Customized Password Speaker Verification
BenZeghiba, Mohamed Faouzi
Bourlard, Hervé
EXTERNAL
https://publications.idiap.ch/attachments/reports/2002/rr02-45.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/benzeghiba-02c
Related documents
Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03)
2003
IDIAP-RR 02-45
Recently we have proposed an approach for user-customized password speaker verification; in this approach, we combined a hybrid HMM/ANN model (used for utterance verification) and a GMM model (used for speaker verification). In this paper, we extend our investigations. First, we propose a new similarity measure that uses confidence measures developed in the HMM/ANN framework. Secondly, we analyze the contribution of each model using a weighted sum combination technique. Experiments conducted on a subset of the PolyVar database show that for a short password the performance of the combined system did not improve significantly compared to the performance using the GMM model alone, and that the HMM/ANN did not contribute much in the combined system. We discuss possible reasons for this.
REPORT
BenZeghiba-02c/IDIAP
Hybrid HMM/ANN and GMM Combination for User-Customized Password Speaker Verification
BenZeghiba, Mohamed Faouzi
Bourlard, Hervé
EXTERNAL
https://publications.idiap.ch/attachments/reports/2002/rr02-45.pdf
PUBLIC
Idiap-RR-45-2002
2002
IDIAP
in Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03,',','),
2003
Recently we have proposed an approach for user-customized password speaker verification; in this approach, we combined a hybrid HMM/ANN model (used for utterance verification) and a GMM model (used for speaker verification). In this paper, we extend our investigations. First, we propose a new similarity measure that uses confidence measures developed in the HMM/ANN framework. Secondly, we analyze the contribution of each model using a weighted sum combination technique. Experiments conducted on a subset of the PolyVar database show that for a short password the performance of the combined system did not improve significantly compared to the performance using the GMM model alone, and that the HMM/ANN did not contribute much in the combined system. We discuss possible reasons for this.