On the impact of the quality of pseudo-labels on the self-supervised speaker verification task

One of the most widely used self-supervised speaker verification system training methods is to optimize the speaker embedding network in a discriminative fashion using clustering algorithm-driven pseudo-labels. Although the pseudo-label-based self-supervised training scheme showed impressive performance, recent studies have shown that label noise can significantly impact the performance. In this paper, we have explored various pseudo-labels driven by different clustering algorithms and conducted a fine-grained analysis of the relationship between the quality of the pseudo-labels and the speaker verification performance. From our experimental results, we shed light on several previously unexplored and overlooked aspects of the pseudo-labels that can have an impact on the speaker verification performance.Moreover, we could observe that the self-supervised speaker verification performance is heavily dependent on multiple qualitative aspects of the clustering algorithm that was used for generating the pseudo-labels. Furthermore, we show that the speaker verification performance can be severely degraded from overfitting to the noisy pseudo-labels and that the mixup strategy can mitigate the memorization effects of label noise.