DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning

Understand

Despite speaker verification has achieved significant performance improvement with the development of deep neural networks, domain mismatch is still a challenging problem in this field.

• In this study, we propose a novel framework to disentangle speaker-related and domain-specific features and apply domain adaptation on the speaker-related feature space solely.
• Instead of performing domain adaptation directly on the feature space where domain information is not removed, using disentanglement can efficiently boost adaptation performance.
• To be specific, our model's input speech from the source and target domains is first encoded into different latent feature spaces.