用语音+大模型的思维链方法，提升阿尔茨海默病早期检测准确率。
Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models
- 通过思维链推理融合语音转文字与大模型分类
- 相比无思维链方法，准确率提升16.7%相对性能
- 适合医疗AI、认知障碍筛查领域研究者参考
全球社会正快速步入超老龄化时代，老年人健康问题日益严峻。人口老龄化加重了国家与家庭的经济负担，痴呆病例随之显著上升。近期基于语音模型和大语言模型（LLM）的研究为痴呆诊断与治疗提供了新路径。本文提出的思维链（CoT）推理方法结合语音与语言模型：首先通过自动语音识别将语音转为文本，再在大语言模型上添加线性层，利用带思维链提示与引导线索的有监督微调（SFT）进行阿尔茨海默病（AD）与非AD分类。该方法相较无思维链提示的方法实现了16.7%的相对性能提升。据我们所知，该方法在思维链类方法中达到了当前最优表现。
原文摘要 · Abstract (English)
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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