%Aigaion2 BibTeX export from Idiap Publications
%Thursday 26 December 2024 09:52:46 PM

@INPROCEEDINGS{BenZeghiba-03-53b,
         author = {BenZeghiba, Mohamed Faouzi and Bourlard, Herv{\'{e}}},
       projects = {Idiap},
          title = {{C}onfidence {M}easures in {M}ultiple pronunciations {M}odeling {F}or {S}peaker {V}erification},
      booktitle = {Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-04)},
           year = {2004},
           note = {IDIAP-RR 03-53},
       crossref = {benzeghiba-03-53},
       abstract = {This paper investigates the use of multiple pronunciations modeling for User-Customized Password Speaker Verification (UCP-SV). The main characteristic of the UCP-SV is that the system does not have any {\it a priori} knowledge about the password used by the speaker. Our aim is to exploit the information about how the speaker pronounces a password in the decision process. This information is extracted automatically by using a speaker-independent speech recognizer. In this paper, we investigate and compare several techniques. Some of them are based on the combination of confidence scores estimated by different models.In this context, we propose a new confidence measure that uses acoustic information extracted during the speaker enrollment and based on {\it log likelihood ratio} measure. These techniques show significant improvement ($15.7\%$ relative improvement in terms of equal error rate) compared to a UCP-SV baseline system where the speaker is modeled by only one model (corresponding to one utterance).},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-53.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-53.ps.gz},
ipdmembership={speech},
}



crossreferenced publications: 
@TECHREPORT{BenZeghiba-03-53,
         author = {BenZeghiba, Mohamed Faouzi and Bourlard, Herv{\'{e}}},
       projects = {Idiap},
          title = {{C}onfidence {M}easures in {M}ultiple pronunciations {M}odeling {F}or {S}peaker {V}erification},
           type = {Idiap-RR},
         number = {Idiap-RR-53-2003},
           year = {2003},
    institution = {IDIAP},
           note = {in Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-04,',','),
 2004},
       abstract = {This paper investigates the use of multiple pronunciations modeling for User-Customized Password Speaker Verification (UCP-SV). The main characteristic of the UCP-SV is that the system does not have any {\it a priori} knowledge about the password used by the speaker. Our aim is to exploit the information about how the speaker pronounces a password in the decision process. This information is extracted automatically by using a speaker-independent speech recognizer. In this paper, we investigate and compare several techniques. Some of them are based on the combination of confidence scores estimated by different models.In this context, we propose a new confidence measure that uses acoustic information extracted during the speaker enrollment and based on {\it log likelihood ratio} measure. These techniques show significant improvement ($15.7\%$ relative improvement in terms of equal error rate) compared to a UCP-SV baseline system where the speaker is modeled by only one model (corresponding to one utterance).},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-53.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-53.ps.gz},
ipdmembership={speech},
}