%Aigaion2 BibTeX export from Idiap Publications %Saturday 21 December 2024 06:19:35 PM @INPROCEEDINGS{BenZeghiba_icassp-03, author = {BenZeghiba, Mohamed Faouzi and Bourlard, Herv{\'{e}}}, projects = {Idiap}, title = {{H}ybrid {HMM/ANN} and {GMM} {C}ombination for User-{C}ustomized {P}assword {S}peaker {V}erification}, booktitle = {Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03)}, year = {2003}, note = {IDIAP-RR 02-45}, crossref = {benzeghiba-02c}, abstract = {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.}, pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-45.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-45.ps.gz}, ipdmembership={speech}, } crossreferenced publications: @TECHREPORT{BenZeghiba-02c, author = {BenZeghiba, Mohamed Faouzi and Bourlard, Herv{\'{e}}}, projects = {Idiap}, title = {{H}ybrid {HMM/ANN} and {GMM} {C}ombination for User-{C}ustomized {P}assword {S}peaker {V}erification}, type = {Idiap-RR}, number = {Idiap-RR-45-2002}, year = {2002}, institution = {IDIAP}, note = {in Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03,',','), 2003}, abstract = {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.}, pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-45.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-45.ps.gz}, ipdmembership={speech}, }