  Visualization is of great value in understanding the internal mechanisms of
neural networks. Previous work found that LayerCAM is a reliable visualization
tool for deep speaker models. In this paper, we use LayerCAM to analyze the
widely-adopted data augmentation (DA) approach, to understand how it leads to
model robustness. We conduct experiments on the VoxCeleb1 dataset for speaker
identification, which shows that both vanilla and activation-based (Act) DA
approaches enhance robustness against interference, with Act DA being
consistently superior. Visualization with LayerCAM suggests DA helps models
learn to delete temporal-frequency (TF) bins that are corrupted by
interference. The `learn to delete' behavior explained why DA models are more
robust than clean models, and why the Act DA is superior over the vanilla DA
when the interference is nontarget speech. However, LayerCAM still cannot
clearly explain the superiority of Act DA in other situations, suggesting
further research.
