# Exploring In-Context Learning Capabilities of ChatGPT for Pathological Speech Detection

arXiv: https://arxiv.org/abs/2503.23873 · HTML: https://mlsanity.com/paper/2503.23873/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 31 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Augmenting Large Audio Language Models with Low-Level Acoustic Features for Dysarthric Speech Detection](https://mlsanity.com/paper/2610.03352/) (2026)

## Abstract (arXiv)

Automatic pathological speech detection approaches have shown promising results, gaining attention as potential diagnostic tools alongside costly traditional methods. While these approaches can achieve high accuracy, their lack of interpretability limits their applicability in clinical practice. In this paper, we investigate the use of multimodal Large Language Models (LLMs), specifically ChatGPT-4o, for automatic pathological speech detection in a few-shot in-context learning setting. Experimental results show that this approach not only delivers promising performance but also provides explanations for its decisions, enhancing model interpretability. To further understand its effectiveness, we conduct an ablation study to analyze the impact of different factors, such as input type and system prompts, on the final results. Our findings highlight the potential of multimodal LLMs for further exploration and advancement in automatic pathological speech detection.