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

Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation