%Aigaion2 BibTeX export from Idiap Publications
%Tuesday 27 February 2024 04:53:53 AM

@INPROCEEDINGS{bengio:2001:icassp,
         author = {Bengio, Samy and Mari{\'{e}}thoz, Johnny},
       projects = {Idiap},
          title = {Learning the Decision Function for Speaker Verification},
      booktitle = {{IEEE} International Conference on Acoustic, Speech, and Signal Processing, {ICASSP}},
           year = {2001},
        address = {Salt Lake, City, USA},
           note = {IDIAP-RR 00-40},
       crossref = {bengio:2000:rr00-40},
       abstract = {This paper explores the possibility to replace the usual thresholding decision rule of log likelihood ratios used in speaker verification systems by more complex and discriminant decision functions based for instance on Linear Regression models or Support Vector Machines. Current speaker verification systems, based on generative models such as HMMs or GMMs, can indeed easily be adapted to use such decision functions. Experiments on both text dependent and text independent tasks always yielded performance improvements and sometimes significantly.},
            pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-40.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-40.ps.gz},
ipdmembership={speech, learning},
}



crossreferenced publications: 
@TECHREPORT{bengio:2000:rr00-40,
         author = {Bengio, Samy and Mari{\'{e}}thoz, Johnny},
       projects = {Idiap},
          title = {Learning the Decision Function for Speaker Verification},
           type = {Idiap-RR},
         number = {Idiap-RR-40-2000},
           year = {2000},
    institution = {IDIAP},
           note = {published in IEEE International Conference on Acoustic, Speech, and Signal Processing},
       abstract = {This paper explores the possibility to replace the usual thresholding decision rule of log likelihood ratios used in speaker verification systems by more complex and discriminant decision functions based for instance on Linear Regression models or Support Vector Machines. Current speaker verification systems, based on generative models such as HMMs or GMMs, can indeed easily be adapted to use such decision functions. Experiments on both text dependent and text independent tasks always yielded performance improvements and sometimes significantly.},
            pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-40.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-40.ps.gz},
ipdmembership={speech, learning},
}