Robust Speaker Verification

Robust speaker verification aims to develop systems that accurately identify speakers even in challenging acoustic conditions, such as background noise or overlapping speech. Current research heavily focuses on improving robustness through techniques like adversarial training, data augmentation (including novel methods like partial additive augmentation and adversarial data augmentation), and self-supervised learning using architectures such as Siamese networks and diffusion probabilistic models. These advancements are crucial for enhancing the reliability and security of voice-based authentication systems and other applications relying on accurate speaker identification.