The deep learning models used for speaker verification are heavily dependent
on large-scale data and correct labels. However, noisy (wrong) labels often
occur, which deteriorates the system's performance. Unfortunately, there are
relatively few studies in this area. In this paper, we propose a method to
gradually filter noisy labels out at the training stage. We compare the network
predictions at different training epochs with ground-truth labels, and select
reliable (considered correct) labels by using the OR gate mechanism like that
in logic circuits. Therefore, our proposed method is named as OR-Gate. We
experimentally demonstrated that the OR-Gate can effectively filter noisy
labels out and has excellent performance.Comment: Submitted to 2023 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2023