Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport
This addresses the challenge of adapting speech enhancement models to new noisy environments without labeled data, which is incremental as it builds on existing domain adaptation methods.
The paper tackles the problem of domain shift in speech enhancement by proposing a discriminator-constrained optimal transport network (DOTN) for unsupervised domain adaptation, which outperforms previous adversarial frameworks on two speech enhancement tasks.
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, by exploiting the knowledge available from the source domain. The domain shift between training and testing data has been reported to be an obstacle to learning problems in diverse fields. Although rich literature exists on unsupervised domain adaptation for classification, the methods proposed, especially in regressions, remain scarce and often depend on additional information regarding the input data. The proposed DOTN approach tactically fuses the optimal transport (OT) theory from mathematical analysis with generative adversarial frameworks, to help evaluate continuous labels in the target domain. The experimental results on two SE tasks demonstrate that by extending the classical OT formulation, our proposed DOTN outperforms previous adversarial domain adaptation frameworks in a purely unsupervised manner.