发表机构
Saskatchewan Polytechnic; Hebei University; University of Alberta; University of Regina（萨斯喀彻温理工学院; 河北大学; 阿尔伯塔大学; 里贾纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于开源LLM的隐私保护语音分析框架LSEAD，在ADReSS系列数据集上使AD分类准确率提升最多5个百分点，为早期AD筛查提供了实用方案。
AI 中文摘要
阿尔茨海默病（AD）的早期诊断对于及时开展干预、延缓疾病进展、改善患者预后至关重要。目前亟需非侵入式且成本效益高的AD检测方法，尤其适用于患者群体多样、录音条件各异的真实临床场景。语音筛查可满足这一需求，其利用无需专用设备采集的自然语音。近年来大语言模型（LLM）的进展通过提供丰富的语言表征和强大的泛化能力，提升了语音分析效果。本研究提出LSEAD，一种基于预训练开源LLM的语音AD检测框架：自动转录语音录音，通过本地部署的LLM提取文本嵌入，应用主成分分析（PCA）降维后进行分类。由于该框架仅依赖语音转录文本和本地部署模型，无需外部数据交换即可支持隐私保护型AD风险评估。在ADReSS20和ADReSSo2021基准数据集上的评估显示，基于LLM的嵌入在不同数据集间泛化性良好，相比现有方法可将AD分类准确率提升最多5个百分点，尤其在早期AD检测中效果显著。这些结果表明，LSEAD为AD早期筛查提供了一种实用、安全且可扩展的方案。
英文摘要
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.
