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The paper introduces Fair-Gate, a novel framework to mitigate sex-related performance gaps in voice biometric systems by addressing demographic shortcut learning and feature entanglement. Fair-Gate uses risk extrapolation to reduce speaker-classification risk variation across sex groups and a local complementary gate to route features into identity and sex branches. Experiments on VoxCeleb1 demonstrate that Fair-Gate improves the utility-fairness trade-off, leading to more sex-fair automatic speaker verification (ASV) performance.
Fair-Gate disentangles speaker identity and sex in voice biometrics, boosting fairness without sacrificing accuracy by explicitly routing features through identity and sex-specific pathways.
Voice biometric systems can exhibit sex-related performance gaps even when overall verification accuracy is strong. We attribute these gaps to two practical mechanisms: (i) demographic shortcut learning, where speaker classification training exploits spurious correlations between sex and speaker identity, and (ii) feature entanglement, where sex-linked acoustic variation overlaps with identity cues and cannot be removed without degrading speaker discrimination. We propose Fair-Gate, a fairness-aware and interpretable risk-gating framework that addresses both mechanisms in a single pipeline. Fair-Gate applies risk extrapolation to reduce variation in speaker-classification risk across proxy sex groups, and introduces a local complementary gate that routes intermediate features into an identity branch and a sex branch. The gate provides interpretability by producing an explicit routing mask that can be inspected to understand which features are allocated to identity versus sex-related pathways. Experiments on VoxCeleb1 show that Fair-Gate improves the utility--fairness trade-off, yielding more sex-fair ASV performance under challenging evaluation conditions.
