{"ID":5438768,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T09:10:46.706950747Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31411","arxiv_id":"2606.31411","title":"Linguistic Bias Mitigation for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck","abstract":"Rapid advancements in generative speech technology have compromised the reliability of voice biometrics. While current spoofing detectors excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. We show that this can be due to linguistic bias. A reliance on linguistic cues observed in training data can then compromise robustness to cross-data. We propose a linguistic-invariant spoofing detection framework utilizing teacher-student adversarial learning. The linguistic-aware teacher model, pre-trained on linguistic content of an external dataset, guides the student detector via gradient reversal to minimize the linguistic information. To prevent the inadvertent removal of non-linguistic cues, we incorporate a Variational Information Bottleneck to enable suppression of principal cues. Across nine DF Arena datasets, our method achieves up to a 36.2% relative reduction in the EER compare to the baseline.","short_abstract":"Rapid advancements in generative speech technology have compromised the reliability of voice biometrics. While current spoofing detectors excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. We show that this can be due to linguistic bias. A reliance on linguistic cues...","url_abs":"https://arxiv.org/abs/2606.31411","url_pdf":"https://arxiv.org/pdf/2606.31411v1","authors":"[\"Anh-Tuan Dao\",\"Driss Matrouf\",\"Mickael Rouvier\",\"Nicholas Evans\"]","published":"2026-06-30T09:36:55Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[]","has_code":false}