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Despite the superior performance deep neural networks have achieved in speaker verification tasks, much of their success benefits from the availability of large-scale and carefully labeled datasets. However, noisy labels often occur during data collection. In this paper, we propose an automatic error correction method for deep speaker embedding learning with noisy labels. Specifically, a label noise correction loss is proposed that leverages a model's generalization capability to correct noisydoi:10.21437/interspeech.2021-2021 dblp:conf/interspeech/TongLLWLH21 fatcat:6ynqdzftlrckhbzuvo3fbu42ey