对抗性重加权用于说话人验证公平性
Adversarial Reweighting for Speaker Verification Fairness
- Amazon Web Services（亚马逊云服务）
- Amazon Alexa AI（亚马逊Alexa人工智能）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出对抗性重加权方法，通过度量学习优化说话人验证公平性，在VoxCeleb上降低性别和国籍子群体的EER差距。
AI中文摘要：
我们使用对抗性重加权（ARW）方法解决说话人验证中的性能公平性问题。ARW被重新表述为用于说话人验证的度量学习，并显示在性别和国籍的不同子群体中改善了结果，而无需在训练数据中标注子群体。对抗网络学习批次中每个训练样本的权重，使得主学习器被迫关注表现不佳的实例。通过使用最小-最大优化算法，该方法提高了整体说话人验证的公平性。我们提出了三种不同的ARW表述：累积成对相似性、伪标签和成对加权，并在VoxCeleb语料库上以等错误率（EER）衡量其性能。结果表明，成对加权方法可以实现总体EER为1.08%，男性为1.25%，女性为0.67%，相对EER降低分别为7.7%、10.1%和3.0%。对于国籍子群体，所提出的算法对美国说话人实现了1.04%的EER，英国说话人为0.76%，所有其他人为1.22%。性别组之间的绝对EER差距从0.70%降至0.58%，而国籍组的标准差从0.21降至0.19。
英文摘要：
We address performance fairness for speaker verification using the adversarial reweighting (ARW) method. ARW is reformulated for speaker verification with metric learning, and shown to improve results across different subgroups of gender and nationality, without requiring annotation of subgroups in the training data. An adversarial network learns a weight for each training sample in the batch so that the main learner is forced to focus on poorly performing instances. Using a min-max optimization algorithm, this method improves overall speaker verification fairness. We present three different ARWformulations: accumulated pairwise similarity, pseudo-labeling, and pairwise weighting, and measure their performance in terms of equal error rate (EER) on the VoxCeleb corpus. Results show that the pairwise weighting method can achieve 1.08% overall EER, 1.25% for male and 0.67% for female speakers, with relative EER reductions of 7.7%, 10.1% and 3.0%, respectively. For nationality subgroups, the proposed algorithm showed 1.04% EER for US speakers, 0.76% for UK speakers, and 1.22% for all others. The absolute EER gap between gender groups was reduced from 0.70% to 0.58%, while the standard deviation over nationality groups decreased from 0.21 to 0.19.
