论文标题
研究说话者验证系统公平性关于英语中代表性不足的口音的研究
Study on the Fairness of Speaker Verification Systems on Underrepresented Accents in English
论文作者
论文摘要
发言人验证（SV）系统目前正在用来做出敏感的决定，例如提供银行帐户访问或决定嫌疑人的声音是否与犯罪者的肇事者相吻合。确保这些系统是公平的，并且不要不利于任何特定群体至关重要。在这项工作中，我们分析了说英语时说话者口音定义的几个群体的几个最先进的SV系统的性能。为此，我们根据Voxceleb语料库策划了一个新数据集，在那里我们仔细从不同国家的口音中选择了说话者的样本。我们使用此数据集评估了几种接受Voxceleb数据训练的SV系统的系统性能。我们表明，虽然歧视性能在重音组之间相当强大，但校准性能在训练数据中表现不佳的某些口音上显着降低。最后，我们表明一种简单的数据平衡方法减轻了这种不良的偏见，当应用于我们最近提供的歧视性条件感知的后端时，特别有效。
Speaker verification (SV) systems are currently being used to make sensitive decisions like giving access to bank accounts or deciding whether the voice of a suspect coincides with that of the perpetrator of a crime. Ensuring that these systems are fair and do not disfavor any particular group is crucial. In this work, we analyze the performance of several state-of-the-art SV systems across groups defined by the accent of the speakers when speaking English. To this end, we curated a new dataset based on the VoxCeleb corpus where we carefully selected samples from speakers with accents from different countries. We use this dataset to evaluate system performance for several SV systems trained with VoxCeleb data. We show that, while discrimination performance is reasonably robust across accent groups, calibration performance degrades dramatically on some accents that are not well represented in the training data. Finally, we show that a simple data balancing approach mitigates this undesirable bias, being particularly effective when applied to our recently-proposed discriminative condition-aware backend.
