Introducing i-vectors for joint anti-spoofing and speaker verification


Any biometric recognizer is vulnerable to direct spoofing attacks and automatic speaker verification (ASV) is no exception; replay, synthesis and conversion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks. Most existing countermeasures use full knowledge of a particular VC system to detect spoofing. We study a potentially more universal approach involving generative modeling perspective. Specifically, we adopt standard ivector representation and probabilistic linear discriminant analysis (PLDA) back-end for joint operation of spoofing attack detector and ASV system. As a proof of concept, we study a vocoder-mismatched ASV and VC attack detection approach on the NIST 2006 speaker recognition evaluation corpus. We report stand-alone accuracy of both the ASV and countermeasure systems as well as their combination using score fusion and joint approach. The method holds promise.

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@inproceedings{Khoury2014IntroducingIF, title={Introducing i-vectors for joint anti-spoofing and speaker verification}, author={Elie el Khoury and Tomi Kinnunen and Aleksandr Sizov and Zhizheng Wu and S{\'e}bastien Marcel}, booktitle={INTERSPEECH}, year={2014} }