  Domain shift poses a significant challenge in cross-domain spoken language
recognition (SLR) by reducing its effectiveness. Unsupervised domain adaptation
(UDA) algorithms have been explored to address domain shifts in SLR without
relying on class labels in the target domain. One successful UDA approach
focuses on learning domain-invariant representations to align feature
distributions between domains. However, disregarding the class structure during
the learning process of domain-invariant representations can result in
over-alignment, negatively impacting the classification task. To overcome this
limitation, we propose an optimal transport (OT)-based UDA algorithm for a
cross-domain SLR, leveraging the distribution geometry structure-aware property
of OT. An OT-based discrepancy measure on a joint distribution over feature and
label information is considered during domain alignment in OT-based UDA. Our
previous study discovered that completely aligning the distributions between
the source and target domains can introduce a negative transfer, where classes
or irrelevant classes from the source domain map to a different class in the
target domain during distribution alignment. This negative transfer degrades
the performance of the adaptive model. To mitigate this issue, we introduce
coupling-weighted partial optimal transport (POT) within our UDA framework for
SLR, where soft weighting on the OT coupling based on transport cost is
adaptively set during domain alignment. A cross-domain SLR task was used in the
experiments to evaluate the proposed UDA. The results demonstrated that our
proposed UDA algorithm significantly improved the performance over existing UDA
algorithms in a cross-channel SLR task.
