Study on the Fairness of Speaker Verification Systems Across Accent and Gender Groups
Study on the Fairness of Speaker Verification Systems Across Accent and Gender Groups
复制标题
跨口音和性别组的说话人验证系统的公平性研究
DOI：
10.1109/icassp49357.2023.10095150
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Luciana Ferrer
中科院分区：
文献类型：
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作者：
Mariel Estevez;Luciana Ferrer
说话人验证 (SV) 系统目前用于执行诸如授予银行帐户访问权限或做出取证决策等后续任务。确保这些系统公平且不偏袒任何特定群体至关重要。在这项工作中，我们分析了两个基于 X 向量的 SV 系统在不同群体中的性能，这些群体是由性别和说话者口音定义的。为此，我们通过从不同国家口音的说话者中选择样本，基于 VoxCeleb 语料库创建了一个新的数据集。我们使用该数据集来评估使用 VoxCeleb 数据训练的 SV 系统的系统性能。我们发现，在训练中代表性不足的群体（女性和非母语英语口音的说话者）中，使用校准敏感指标测量的表现明显下降。最后，我们证明了一种简单的数据平衡方法可以减轻少数群体的这种不良偏见，而不会降低多数群体的表现。
Speaker verification (SV) systems are currently used for consequential tasks like giving access to bank accounts or making forensic decisions. Ensuring that these systems are fair and do not disfavor any particular group is crucial. In this work, we analyze the performance of two X-vector-based SV systems across groups defined by gender and accent of the speakers when speaking English. To this end, we created a new dataset based on the VoxCeleb corpus by selecting samples from speakers with accents from different countries. We used this dataset to evaluate system performance of SV systems trained with VoxCeleb data. We show that performance, measured with a calibration-sensitive metric, is markedly degraded on groups that are underrepresented in training: females and speakers with nonnative accents in English. Finally, we show that a simple data balancing approach mitigates this undesirable bias on the minority groups without degrading performance on the majority groups.