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Speaker-Targeted Synthetic Speech Detection

Text-to-speech (TTS) and voice conversion (VC) technologies are evolving quickly towards realistic-sounding human-like voices. As this technology improves, so does the opportunity for malpractice in speaker identiﬁcation (SID) via spooﬁng, the process of impersonating a voice biometric via synthesis. More data typically equates to a more realistic voice model, which poses an issue for well-known subjects, such as politicians and celebrities, who have vast amounts of multimedia available online. Detection of synthetic speech has relied on signal processing techniques that focus on the generation of new acoustic features and train deep-learning models to detect when an audio ﬁle has been manipulated through the characterization of unnatural changes or artifacts. However, these techniques do not use any information from the speaker they are evaluating. This paper proposes to incorporate information from the speaker-of-interest (SoI) into the models to avoid speciﬁc spooﬁng attacks for certain vulnerable people as a logical access (LA) control tool. The wealth of data for well-known people can also be used to train a speaker-speciﬁc spooﬁng detector with a higher level of accuracy than a speaker-independent model. The paper proposes a new xResNet-PLDA system and compares it to three different baseline systems: a state-of-the-art speaker identiﬁcation system, an xResNet system trained to discriminate between bonaﬁde and fake speech, and a speaker identiﬁcation system in which the PLDA and calibration models were trained with bonaﬁde and fake speech. We evaluated the systems in two different scenarios — a cross-validation scenario and a hold-out scenario — with three different databases. We show how the proposed system outperforms dramatically the baseline systems in each scenario and for each database. Finally, we show how using a small amount of the SoI’s speech to adapt global calibration parameters improves the performance of the system, especially in unseen conditions.