  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.
