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

By Hemlata Tak, Massimiliano Todisco, Xin Eric Wang, Jee-weon Jung, Junichi Yamagishi, N. Evans. The Speaker and Language Recognition Workshop, 2022

321 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1d38b028c7db875d0b4058b4a429a792dfeec527

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Citations in the corpus

ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2022, methodology): “%) for the ASVspoof 2021 LA database [42].”