# Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation

arXiv: https://arxiv.org/abs/1912.04370 · HTML: https://mlsanity.com/paper/1912.04370/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Careful Whisper -- leveraging advances in automatic speech recognition for robust and interpretable aphasia subtype classification](https://mlsanity.com/paper/2308.01327/) (2023)
- [A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning](https://mlsanity.com/paper/2305.13331/) (2023)
- [Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances](https://mlsanity.com/paper/2405.15441/) (2024)
- [Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition](https://mlsanity.com/paper/2204.00448/) (2022)
- [Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift](https://mlsanity.com/paper/2312.17463/) (2023)

## Abstract (arXiv)

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).