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         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,',','),
       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},