Towards generalisable and calibrated synthetic speech detection with self-supervised representations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913389009174528 |
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| author | Pascu, Octavian Stan, Adriana Oneata, Dan Oneata, Elisabeta Cucu, Horia |
| author_facet | Pascu, Octavian Stan, Adriana Oneata, Dan Oneata, Elisabeta Cucu, Horia |
| contents | Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current audio deepfake models fall short of this desideratum. In this work we investigate the potential of pretrained self-supervised representations in building general and calibrated audio deepfake detection models. We show that large frozen representations coupled with a simple logistic regression classifier are extremely effective in achieving strong generalisation capabilities: compared to the RawNet2 model, this approach reduces the equal error rate from 30.9% to 8.8% on a benchmark of eight deepfake datasets, while learning less than 2k parameters. Moreover, the proposed method produces considerably more reliable predictions compared to previous approaches making it more suitable for realistic use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05384 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards generalisable and calibrated synthetic speech detection with self-supervised representations Pascu, Octavian Stan, Adriana Oneata, Dan Oneata, Elisabeta Cucu, Horia Audio and Speech Processing Sound Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current audio deepfake models fall short of this desideratum. In this work we investigate the potential of pretrained self-supervised representations in building general and calibrated audio deepfake detection models. We show that large frozen representations coupled with a simple logistic regression classifier are extremely effective in achieving strong generalisation capabilities: compared to the RawNet2 model, this approach reduces the equal error rate from 30.9% to 8.8% on a benchmark of eight deepfake datasets, while learning less than 2k parameters. Moreover, the proposed method produces considerably more reliable predictions compared to previous approaches making it more suitable for realistic use. |
| title | Towards generalisable and calibrated synthetic speech detection with self-supervised representations |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2309.05384 |