Cog-TiPRO：利用大语言模型迭代优化提示词，通过纵向语音助手指令检测认知衰退
Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands
AI总结：
本研究提出Cog-TiPRO框架，结合LLM迭代提示优化、HuBERT声学特征提取与iTransformer时序建模，通过纵向分析老年人居家语音助手指令检测认知衰退，MCI检测准确率达73.80%，较基线提升27.13%。
摘要：
认知衰退的早期检测对于开展可延缓神经退行性疾病进展的干预措施至关重要。传统诊断方法依赖耗时费力的临床评估，难以实现频繁监测。本试点研究探索将语音助手系统（VAS）作为非侵入性工具，通过纵向分析语音指令中的言语模式来检测认知衰退。在18个月的研究周期内，我们收集了35名老年人的语音指令，其中15名参与者提供了日常居家的VAS交互数据。为解决这些指令简短、非结构化且存在噪声的分析难题，我们提出Cog-TiPRO框架，该框架结合了三大模块：（1）由大语言模型驱动的迭代提示词优化方法，用于提取语言特征；（2）基于HuBERT的声学特征提取；（3）基于transformer的时序建模。借助iTransformer，我们的方法在检测轻度认知障碍（MCI）时达到73.80%的准确率和72.67%的F1值，较基线模型性能提升27.13%。通过我们的大语言模型方法，我们识别出了认知衰退个体日常指令使用模式中特有的语言特征。
英文摘要：
Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnostic approaches rely on labor-intensive clinical assessments, which are impractical for frequent monitoring. Our pilot study investigates voice assistant systems (VAS) as non-invasive tools for detecting cognitive decline through longitudinal analysis of speech patterns in voice commands. Over an 18-month period, we collected voice commands from 35 older adults, with 15 participants providing daily at-home VAS interactions. To address the challenges of analyzing these short, unstructured and noisy commands, we propose Cog-TiPRO, a framework that combines (1) LLM-driven iterative prompt refinement for linguistic feature extraction, (2) HuBERT-based acoustic feature extraction, and (3) transformer-based temporal modeling. Using iTransformer, our approach achieves 73.80% accuracy and 72.67% F1-score in detecting MCI, outperforming its baseline by 27.13%. Through our LLM approach, we identify linguistic features that uniquely characterize everyday command usage patterns in individuals experiencing cognitive decline.
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