Interpreting SSL Representations for Spoof Detection: a WavLM Study
Self-supervised speech models such as WavLM achieve strong spoofing detection performance, yet which layers encode the most spoof-relevant information and what acoustic properties they capture remain opaque. We present a layer-wise interpretability study of WavLM using the ASVspoof 5 database, combining three complementary analyses: (i) per-layer linear probing with attack-level evaluation across speech synthesis, voice conversion, and adversarial attacks; (ii) a canonical correlation analysis (CCA) linking layer representations to spoofing-relevant acoustic features; (iii) an exhaustive grid search over layer subsets showing that use of five layers outperforms full thirteen-layer pooling...
| Authors | Mohamed Mallat, Michele Panariello, Massimiliano Todisco et al. |
|---|---|
| Primary category | cs.SD |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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