The authors propose a linguistic-invariant spoofing detection framework that addresses poor generalization in out-of-domain settings caused by reliance on linguistic cues. The method uses a teacher-student adversarial learning approach where a linguistic-aware teacher guides the student detector via gradient reversal to minimize linguistic information.
- A Variational Information Bottleneck is incorporated to prevent the inadvertent removal of non-linguistic cues while suppressing principal linguistic ones.
- The framework was evaluated across nine DF Arena datasets.
- The method achieves up to a 36.2% relative reduction in Equal Error Rate (EER) compared to the baseline.
This approach improves robustness to cross-data scenarios by decoupling spoofing detection from specific linguistic content.