Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection
Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals. We hypothesise that these constraints manifest as structured trajectory dynamics in the latent space of Self-Supervised Learning (SSL) models, and that synthetic speech violates them detectably. To test this hypothesis, we train a causal Long Short-Term Memory (LSTM) next-frame predictor on bonafide speech only (Stage 1), using the deepfake-specialised SSL backbone Wav2Vec2-Large-AntiDeepfake, and compare against a static global-average-pooling baseline using identical features, thus isolating the contribution of temporal modelling. A supervised Stage 2, which trains...
| Authors | Tomás Andrade Weber |
|---|---|
| Primary category | eess.AS |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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