4 accepted papers
This paper proposes the contrastive Gaussian-Dirac diffusion (CGDD) model, which introduces a novel approach to hybrid noise diffusion by combining continuous and discrete noise processes for text generation tasks. Leveraging the contrastive learning, the proposed CGDD structures the embedding space…
Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utiliz…
It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives. Although network models have the potential to be a surrogate for complex human hearing perception, they may contain instabilities in…
This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. The DOTN aims to estimate clean references of noisy speech in a target domain,…