Visualizing data augmentation in deep speaker recognition
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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.
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