Abstract
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.
Abstract (translated)
声纹系统可能会表现出性别相关的性能差距，即使其总体验证准确性很强。我们把这种差距归因于两种实际机制：(i) 人口统计捷径学习，在此过程中，说话人分类训练利用了性别与说话人身份之间的虚假关联；(ii) 特征纠缠，其中与性别的声学变异重叠了身份线索，这些特征无法在不降低说话人识别准确性的情况下被移除。我们提出了一种公平感知且可解释的风险门控框架——Fair-Gate，该框架在一个单一的工作流程中解决了这两种机制。Fair-Gate 应用了风险外推法来减少不同代理性别组间的说话人分类风险差异，并引入了一个局部互补门，将中间特征路由至身份分支和性别分支。此门提供了可解释性，因为它生成了可以被检查的明确路由掩码，以了解哪些特征分配给了身份路径与性别相关的路径。在 VoxCeleb1 数据集上的实验表明，Fair-Gate 改善了效用-公平性的权衡，在具有挑战性的评估条件下实现了更公平的声纹认证性能。
URL
https://arxiv.org/abs/2603.11360
