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arXiv · 2506.01683

Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models

Chanwoo Park·Anna Seo Gyeong Choi·Sunghye Cho·Chanwoo Kim
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Abstract

Societies worldwide are rapidly entering a super-aged era, making elderly health a pressing concern. The aging population is increasing the burden on national economies and households. Dementia cases are rising significantly with this demographic shift. Recent research using voice-based models and large language models (LLM) offers new possibilities for dementia diagnosis and treatment. Our Chain-of-Thought (CoT) reasoning method combines speech and language models. The process starts with automatic speech recognition to convert speech to text. We add a linear layer to an LLM for Alzheimer's disease (AD) and non-AD classification, using supervised fine-tuning (SFT) with CoT reasoning and cues. This approach showed an 16.7% relative performance improvement compared to methods without CoT prompt reasoning. To the best of our knowledge, our proposed method achieved state-of-the-art performance in CoT approaches.

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Chanwoo Park, Anna Seo Gyeong Choi, Sunghye Cho, Chanwoo Kim. 2025-06-02. Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models. https://doi.org/10.5281/zenodo.15511013

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