Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912409516507136 |
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| author | Park, Chanwoo Choi, Anna Seo Gyeong Cho, Sunghye Kim, Chanwoo |
| author_facet | Park, Chanwoo Choi, Anna Seo Gyeong Cho, Sunghye Kim, Chanwoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models Park, Chanwoo Choi, Anna Seo Gyeong Cho, Sunghye Kim, Chanwoo Artificial Intelligence 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. |
| title | Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.01683 |