The success of deep learning requires high-quality annotated and massive data. However, the size and the quality of a dataset are usually a trade-off in practice, as data collection and cleaning are expensive and time-consuming. In real-world applications, especially those using crowdsourcing datasets, it is important to exclude noisy labels. To address this, this paper proposes an automatic noisy label detection (NLD) technique with inconsistency ranking for high-quality data. We apply this technique to the automatic speaker verification (ASV) task as a proof of concept. We investigate both inter-class and intra-class inconsistency ranking and compare several metric learning loss functions under different noise settings. Experimental results confirm that the proposed solution could increase both the efficient and effective cleaning of large-scale speaker recognition datasets.

翻译：深度学习的发展需要高质量标注的大规模数据。然而在实践中，数据集规模与质量往往存在权衡取舍，因为数据采集与清洗成本高昂且耗时。在现实应用场景中，尤其是采用众包数据集的场景，排除噪声标签至关重要。针对这一问题，本文提出一种基于不一致性排名的自动噪声标签检测技术，旨在获取高质量数据。我们将该技术应用于自动说话人确认任务作为概念验证，分别研究了类间与类内不一致性排名，并在不同噪声设置下比较了多种度量学习损失函数。实验结果表明，所提方案能够提升大规模说话人识别数据集清洗的效率与有效性。