Alethia: A Foundational Encoder for Voice Deepfakes
Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, downstream finetuning has now reached a state of diminishing returns. In this paper, we shift the focus to pretraining and propose a novel recipe that combines bottleneck masked embedding prediction with flow-matching based spectrogram reconstruction. The outcome, Alethia, is the first foundational audio encoder for various voice deepfake detection and localization tasks. We evaluate on $5$ different tasks with $56$ benchmark datasets, and note Alethia significantly outperforms state-of-the-art SFMs with superior robustness to real-world pertur...
| Authors | Yi Zhu, Brahmi Dwivedi, Jayaram Raghuram et al. |
|---|---|
| Primary category | cs.SD |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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