  Noisy labels are inevitable, even in well-annotated datasets. The detection
of noisy labels is of significant importance to enhance the robustness of
speaker recognition models. In this paper, we propose a novel noisy label
detection approach based on two new statistical metrics: Continuous
Inconsistent Counting (CIC) and Total Inconsistent Counting (TIC). These
metrics are calculated through Cross-Epoch Counting (CEC) and correspond to the
early and late stages of training, respectively. Additionally, we categorize
samples based on their prediction results into three categories: inconsistent
samples, hard samples, and easy samples. During training, we gradually increase
the difficulty of hard samples to update model parameters, preventing noisy
labels from being overfitted. Compared to contrastive schemes, our approach not
only achieves the best performance in speaker verification but also excels in
noisy label detection.
