  In order to reduce domain discrepancy to improve the performance of
cross-domain spoken language identification (SLID) system, as an unsupervised
domain adaptation (UDA) method, we have proposed a joint distribution alignment
(JDA) model based on optimal transport (OT). A discrepancy measurement based on
OT was adopted for JDA between training and test data sets. In our previous
study, it was supposed that the training and test sets share the same label
space. However, in real applications, the label space of the test set is only a
subset of that of the training set. Fully matching training and test domains
for distribution alignment may introduce negative domain transfer. In this
paper, we propose an JDA model based on partial optimal transport (POT), i.e.,
only partial couplings of OT are allowed during JDA. Moreover, since the label
of test data is unknown, in the POT, a soft weighting on the coupling based on
transport cost is adaptively set during domain alignment. Experiments were
carried out on a cross-domain SLID task to evaluate the proposed UDA. Results
showed that our proposed UDA significantly improved the performance due to the
consideration of the partial couplings in OT.
