  Data augmentation (DA) has gained widespread popularity in deep speaker
models due to its ease of implementation and significant effectiveness. It
enriches training data by simulating real-life acoustic variations, enabling
deep neural networks to learn speaker-related representations while
disregarding irrelevant acoustic variations, thereby improving robustness and
generalization. However, a potential issue with the vanilla DA is augmentation
residual, i.e., unwanted distortion caused by different types of augmentation.
To address this problem, this paper proposes a novel approach called
adversarial data augmentation (A-DA) which combines DA with adversarial
learning. Specifically, it involves an additional augmentation classifier to
categorize various augmentation types used in data augmentation. This
adversarial learning empowers the network to generate speaker embeddings that
can deceive the augmentation classifier, making the learned speaker embeddings
more robust in the face of augmentation variations. Experiments conducted on
VoxCeleb and CN-Celeb datasets demonstrate that our proposed A-DA outperforms
standard DA in both augmentation matched and mismatched test conditions,
showcasing its superior robustness and generalization against acoustic
variations.
