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Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation

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arxiv 2202.12233 v2 pith:FZKXDZJ7 submitted 2022-02-24 eess.AS cs.SD

Hemlata Tak , Massimiliano Todisco , Xin Wang , Jee-weon Jung , Junichi Yamagishi , Nicholas Evans This is my paper · ORCID
classification eess.AScs.SD
keywords dataattacksaugmentationdeepfakespoofingwav2vecaccessalmost
verification ladder T0 review T1 audit T2 compute T3 formal
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The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data. With this usually being limited, current solutions typically lack generalisation to attacks encountered in the wild. Strategies to improve reliability in the face of uncontrolled, unpredictable attacks are hence needed. We report in this paper our efforts to use self-supervised learning in the form of a wav2vec 2.0 front-end with fine tuning. Despite initial base representations being learned using only bona fide data and no spoofed data, we obtain the lowest equal error rates reported in the literature for both the ASVspoof 2021 Logical Access and Deepfake databases. When combined with data augmentation,these results correspond to an improvement of almost 90% relative to our baseline system.

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Forward citations

Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Adapted LALMs can reach competitive spoofing-aware speaker verification (89.3% accuracy, 0.19 min a-DCF on an ASVspoof5 subset), though zero-shot performance is near chance.

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    cs.SD 2025-11 conditional novelty 6.0 of 10

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