Linguistic Bias Mitigation for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

2026-06-30

AI Summary

This study addresses the poor generalization of existing voice anti-spoofing models in cross-domain scenarios, which primarily stems from their reliance on linguistic content—referred to as language bias. The work identifies language bias for the first time as a key factor degrading cross-domain performance and proposes a language-invariant detection framework. This framework leverages a pretrained language-aware teacher model to guide a student model, integrating a gradient reversal layer with a variational information bottleneck to suppress language-related cues while preserving discriminative acoustic features. Evaluated on nine DF-Audio datasets, the proposed method significantly outperforms baseline approaches, achieving a relative reduction in equal error rate (EER) of up to 36.2%.

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.

Problem

• linguistic bias
• spoofing detection
• out-of-domain generalization
• voice biometrics
• generalization

Innovation

• Linguistic Bias Mitigation
• Gradient Reversal
• Variational Information Bottleneck
• Spoofing Detection
• Adversarial Learning