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.
CCA analysis reveals that, while most acoustic features peak in alignment with mid-layers and then decay, voice quality measures jitter, shimmer, and the harmonics-to-noise ratio show the opposite trend, with correlation increasing toward later layers.
Embedding visualisations reveal that deeper layers capture a continuous acoustic similarity among attacks rather than discrete categories, suggesting these layers encode fundamental synthesis artifacts rather than attack-specific signatures.