Multilingual Voice Impersonation Dataset and Evaluation

Well-known vulnerabilities of voice-based biometrics are impersonation, replay attacks, artificial signals/speech synthesis, and voice conversion. Among these, voice impersonation is the obvious and simplest way of attack that can be performed. Though voice impersonation by amateurs is considered no...

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Bibliographic Details
Main Authors: Mandalapu, Hareesh, Busch, Christoph, Ramachandra, Raghavendra
Format: Book
Language:English
Online Access:Request full text
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Summary:Well-known vulnerabilities of voice-based biometrics are impersonation, replay attacks, artificial signals/speech synthesis, and voice conversion. Among these, voice impersonation is the obvious and simplest way of attack that can be performed. Though voice impersonation by amateurs is considered not a severe threat to ASV systems, studies show that professional impersonators can successfully influence the performance of the voice-based biometrics system. In this work, we have created a novel voice impersonation attack dataset and studied the impact of voice impersonation on automatic speaker verification systems. The dataset consisting of celebrity speeches from 3 different languages, and their impersonations are acquired from YouTube. The vulnerability of speaker verification is observed among all three languages on both the classical i-vector based method and the deep neural network-based x-vector method.