Introducing i-vectors for joint anti-spoofing and speaker verification
Elie Khoury, Tomi Kinnunen, А. С. Сизов, Zhizheng Wu, Sébastien Marcel · 2014
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.