A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection
Self-supervised learning (SSL) has transformed speech processing, with benchmarks such as SUPERB establishing fair comparisons across diverse downstream tasks. Despite it's security-critical importance, Audio deepfake detection has remained outside these efforts. In this work, we introduce Spoof-SUPERB, a benchmark for audio deepfake detection that systematically evaluates 20 SSL models spanning generative, discriminative, and spectrogram-based architectures. We evaluated these models on multiple in-domain and out-of-domain datasets. Our results reveal that large-scale discriminative models such as XLS-R, UniSpeech-SAT, and WavLM Large consistently outperform other models, benefiting from...
| Authors | Hashim Ali, Nithin Sai Adupa, Surya Subramani et al. |
|---|---|
| Primary category | eess.AS |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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