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arXiv · 2303.01126

Speaker-Aware Anti-Spoofing

Xuechen Liu·Md Sahidullah·Kong Aik Lee·Tomi Kinnunen
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Abstract

We address speaker-aware anti-spoofing, where prior knowledge of the target speaker is incorporated into a voice spoofing countermeasure (CM). In contrast to the frequently used speaker-independent solutions, we train the CM in a speaker-conditioned way. As a proof of concept, we consider speaker-aware extension to the state-of-the-art AASIST (audio anti-spoofing using integrated spectro-temporal graph attention networks) model. To this end, we consider two alternative strategies to incorporate target speaker information at the frame and utterance levels, respectively. The experimental results on a custom protocol based on ASVspoof 2019 dataset indicates the efficiency of the speaker information via enrollment: we obtain maximum relative improvements of 25.1% and 11.6% in equal error rate (EER) and minimum tandem detection cost function (t-DCF) over a speaker-independent baseline, respectively.

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BibTeXRIS

Xuechen Liu, Md Sahidullah, Kong Aik Lee, Tomi Kinnunen. 2023-03-02. Speaker-Aware Anti-Spoofing. https://arxiv.org/abs/2303.01126

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