  The deep learning models used for speaker verification rely heavily on large
amounts of data and correct labeling. However, noisy (incorrect) labels often
occur, which degrades the performance of the system. In this paper, we propose
a novel two-stage learning method to filter out noisy labels from speaker
datasets. Since a DNN will first fit data with clean labels, we first train the
model with all data for several epochs. Then, based on this model, the model
predictions are compared with the labels using our proposed the OR-Gate with
top-k mechanism to select the data with clean labels and the selected data is
used to train the model. This process is iterated until the training is
completed. We have demonstrated the effectiveness of this method in filtering
noisy labels through extensive experiments and have achieved excellent
performance on the VoxCeleb (1 and 2) with different added noise rates.
