Adversarial Data Augmentation for Robust Speaker Verification

• URL: http://arxiv.org/abs/2402.02699v1
• Date: Mon, 5 Feb 2024 03:23:34 GMT
• Title: Adversarial Data Augmentation for Robust Speaker Verification
• Authors: Zhenyu Zhou and Junhui Chen and Namin Wang and Lantian Li and Dong Wang
• Abstract summary: This paper proposes a novel approach called adversarial data augmentation (A-DA) It involves an additional augmentation classifier to categorize various augmentation types used in data augmentation. 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.
• Score: 17.40709301417885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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