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Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR
arXiv – CS AI|Ajinkya Kulkarni, Sandipana Dowerah, Atharva Kulkarni, Tanel Alum\"ae, Mathew Magimai Doss|
🤖AI Summary
Researchers introduced RAPTOR, a study comparing compact SSL models for audio deepfake detection, finding that multilingual HuBERT pre-training enables smaller 100M parameter models to match larger commercial systems. The study reveals that pre-training approach matters more than model size, with WavLM variants showing overconfident miscalibration issues compared to HuBERT models.
Key Takeaways
- →Compact 100M parameter models with multilingual HuBERT pre-training can match the performance of larger commercial deepfake detection systems.
- →SSL pre-training trajectory is more important than model scale for reliable audio deepfake detection.
- →WavLM variants exhibit overconfident miscalibration under perturbation while HuBERT models remain stable.
- →The study evaluated 14 cross-domain benchmarks using a unified pairwise-gated fusion detector framework.
- →Test-time augmentation protocols revealed calibration differences invisible to standard evaluation metrics.
Read Original →via arXiv – CS AI
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