  Multi-language speech datasets are scarce and often have small sample sizes
in the medical domain. Robust transfer of linguistic features across languages
could improve rates of early diagnosis and therapy for speakers of low-resource
languages when detecting health conditions from speech. We utilize
out-of-domain, unpaired, single-speaker, healthy speech data for training
multiple Optimal Transport (OT) domain adaptation systems. We learn mappings
from other languages to English and detect aphasia from linguistic
characteristics of speech, and show that OT domain adaptation improves aphasia
detection over unilingual baselines for French (6% increased F1) and Mandarin
(5% increased F1). Further, we show that adding aphasic data to the domain
adaptation system significantly increases performance for both French and
Mandarin, increasing the F1 scores further (10% and 8% increase in F1 scores
for French and Mandarin, respectively, over unilingual baselines).
