Comparing Self-Supervised and Domain-Invariant Features for Cross-Domain Voice Phishing Detection
Voice phishing detection faces three critical challenges: real criminal recordings are unavailable due to privacy constraints; when available, only a handful of samples exist, insufficient for fine-tuning; and lightweight acoustic-only detection is needed as an alternative to large self-supervised models. We compare domain-invariant prosodic features and self-supervised representations (HuBERT, wav2vec2.0) through cross-domain evaluation-training on scenario-based actor recordings and testing on authentic criminal calls. Domain-invariant prosodic features achieve 69.5% F1 zero-shot and 71.0% with 5-shot learning. HuBERT achieves highest performance (94.2% F1, 5-shot), while wav2vec2.0 exh...
| Authors | Jeongmin Lee, Seung Yun, Minkyu Lee et al. |
|---|---|
| Primary category | cs.SD |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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