这个框架直接用开源LLM做语音筛查，不用联网传数据，保护隐私，在ADReSS20和ADReSSo2021上还比现有方法最高涨5个点，适合做早期阿尔茨海默病筛查研究的人看。
研究提出LSEAD框架，使用预训练开源大语言模型对语音录音自动转录并提取文本嵌入，再通过主成分分析降维后进行分类。该方法仅依赖本地部署的模型和语音转录文本，无需外部数据交换，支持隐私保护的AD风险评估。在ADReSS20和ADReSSo2021基准数据集上，LLM嵌入相比现有方法将AD分类准确率最高提升5%，尤其对早期检测效果显著。
LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening
Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes. There is a growing need for AD detection methods that are non-invasive and cost-effective, especially in real-world clinical settings with diverse patient populations and recording conditions. Speech-based screening addresses these needs by using natural speech collected without specialized equipment. Recent advances in large language models (LLMs) have improved speech analysis by providing rich linguistic representations and strong generalization. In this study, we propose LSEAD, a speech-based AD detection framework using pretrained open-source LLMs. Speech recordings are automatically transcribed, and text embeddings are extracted using locally deployed LLMs. Principal component analysis (PCA) is applied to reduce dimensionality before classification. Because the framework relies only on speech transcripts and locally deployed models, it supports privacy-preserving AD risk assessment without external data exchange. We evaluate LSEAD on the ADReSS20 and ADReSSo2021 benchmark datasets. Experimental results show that LLM-based embeddings generalize well across datasets and improve AD classification accuracy by up to 5 percent over existing methods, especially for early-stage detection. These results demonstrate that LSEAD provides a practical, secure, and scalable approach for early AD screening.