Evaluation of Bayes Decision Approach to Automatic Determination of Thresholds for Speaker Verification
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Auteurs : Y. GongSource :
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Abstract
Under Bayes statistical decision framework, this paper addresses statistical modelling and determination of thresholds for speaker verification systems. It is pointed out that speaker-dependent between-speaker score distribution is bi-modal, as opposed to common believe that the distribution is normal. Previous mono-modal modelling of between-speaker score distribution is then extended to bi-modal modelling. For a text-dependent application, experiments are reported which compare verification results with speaker-independent unique threshold, speaker-dependent mono-modal distributions and speaker-dependent bi-modal distributions. It is observed that speaker-dependent thresholds give dramatic error reduction, as compared to unique threshold and that bi-modal and mono-modal distribution models give very close verification results. For a 200 speaker database, using 1 sec of test speech, the resulting system resulted in a 0.65========percnt; mean verification error.
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<front><div type="abstract" xml:lang="en" wicri:score="1099">Under Bayes statistical decision framework, this paper addresses statistical modelling and determination of thresholds for speaker verification systems. It is pointed out that speaker-dependent between-speaker score distribution is bi-modal, as opposed to common believe that the distribution is normal. Previous mono-modal modelling of between-speaker score distribution is then extended to bi-modal modelling. For a text-dependent application, experiments are reported which compare verification results with speaker-independent unique threshold, speaker-dependent mono-modal distributions and speaker-dependent bi-modal distributions. It is observed that speaker-dependent thresholds give dramatic error reduction, as compared to unique threshold and that bi-modal and mono-modal distribution models give very close verification results. For a 200 speaker database, using 1 sec of test speech, the resulting system resulted in a 0.65========percnt; mean verification error.</div>
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<BibTex type="inproceedings"><ref>gong95c</ref>
<crinnumber>95-R-277</crinnumber>
<category>3</category>
<equipe>RFIA</equipe>
<author><e>Gong, Y.</e>
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<title>Evaluation of Bayes Decision Approach to Automatic Determination of Thresholds for Speaker Verification</title>
<booktitle>{Proceedings 4th European Conference on Speech Communication and Technology, Madrid (Spain)}</booktitle>
<year>1995</year>
<volume>1</volume>
<pages>367-370</pages>
<month>sep</month>
<keywords><e>speech recognition</e>
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<abstract>Under Bayes statistical decision framework, this paper addresses statistical modelling and determination of thresholds for speaker verification systems. It is pointed out that speaker-dependent between-speaker score distribution is bi-modal, as opposed to common believe that the distribution is normal. Previous mono-modal modelling of between-speaker score distribution is then extended to bi-modal modelling. For a text-dependent application, experiments are reported which compare verification results with speaker-independent unique threshold, speaker-dependent mono-modal distributions and speaker-dependent bi-modal distributions. It is observed that speaker-dependent thresholds give dramatic error reduction, as compared to unique threshold and that bi-modal and mono-modal distribution models give very close verification results. For a 200 speaker database, using 1 sec of test speech, the resulting system resulted in a 0.65========percnt; mean verification error.</abstract>
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