Inconsistency Ranking-based Noisy Label Detection for High-quality Data
This addresses the challenge of data quality for applications using crowdsourced datasets, but it is incremental as it builds on existing noisy label detection techniques.
The paper tackled the problem of noisy labels in datasets by proposing an inconsistency ranking-based detection method, which increased the efficiency and effectiveness of cleaning large-scale speaker recognition datasets.
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.