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Revisiting the security of speaker verification systems against imposture using synthetic speech
In this paper, we investigate imposture using synthetic speech.
Although this problem was first examined over a decade ago,
dramatic improvements in both speaker verification (SV) and
speech synthesis have renewed interest in this problem. We
use a HMM-based speech synthesizer which creates synthetic
speech for a targeted speaker through adaptation of a background
model. We use two SV systems: standard GMMUBM-
based and a newer SVM-based. Our results show when
the systems are tested with human speech, there are zero false
acceptances and zero false rejections. However, when the systems
are tested with synthesized speech, all claims for the targeted
speaker are accepted while all other claims are rejected.
We propose a two-step process for detection of synthesized
speech in order to prevent this imposture. Overall, while SV
systems have impressive accuracy, even with the proposed detector,
high-quality synthetic speech will lead to an unacceptably
high false acceptance rate