Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
This work addresses early diagnosis and therapy for speakers of low-resource languages by improving aphasia detection, though it is incremental as it builds on existing domain adaptation methods.
The paper tackled the problem of detecting aphasia from speech in low-resource languages by using Optimal Transport domain adaptation to transfer linguistic features across languages, resulting in improved F1 scores, such as a 10% increase for French and 8% for Mandarin over unilingual baselines.
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).