# Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2204.00448 · HTML: https://mlsanity.com/paper/2204.00448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Scaling ASR Improves Zero and Few Shot Learning](https://mlsanity.com/paper/2111.05948/) (2021)
- [Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation](https://mlsanity.com/paper/1912.04370/) (2019)
- [BlaBla: Linguistic Feature Extraction for Clinical Analysis in Multiple Languages](https://mlsanity.com/paper/2005.10219/) (2020)

## Built on by (most cited first, 3 of 3)

- [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)
- [AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition](https://mlsanity.com/paper/2506.06566/) (2025)

## Abstract (arXiv)

Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. Detecting and assessing Aphasia in patients is a difficult, time-consuming process, and numerous attempts to automate it have been made, the most successful using machine learning models trained on aphasic speech data. Like in many medical applications, aphasic speech data is scarce and the problem is exacerbated in so-called "low resource" languages, which are, for this task, most languages excluding English. We attempt to leverage available data in English and achieve zero-shot aphasia detection in low-resource languages such as Greek and French, by using language-agnostic linguistic features. Current cross-lingual aphasia detection approaches rely on manually extracted transcripts. We propose an end-to-end pipeline using pre-trained Automatic Speech Recognition (ASR) models that share cross-lingual speech representations and are fine-tuned for our desired low-resource languages. To further boost our ASR model's performance, we also combine it with a language model. We show that our ASR-based end-to-end pipeline offers comparable results to previous setups using human-annotated transcripts.