机器人对话系统可实时检测痴呆患者的言语生物标志物，辅助认知评估。
Developing Conversational Speech Systems for Robots to Detect Speech Biomarkers of Cognition in People Living with Dementia
- 基于大语言模型的实时对话系统，1.5秒内完成响应与分析。
- 六项言语标志物与MMSE评分中度相关，综合评分表现更优。
- 适合临床研究与痴呆早期筛查，尤其关注人机对话场景差异。
本研究开发并测试了一套用于机器人对话的语音系统，旨在检测痴呆患者（PLwD）的认知受损言语生物标志物。系统采用后端Python WebSocket服务器与核心模块，集成经痴呆领域微调的大语言模型（LLM），实现实时处理用户输入并生成机器人回应，响应时间小于1.5秒。前端为渐进式网页应用（PWA），可在手机上实时显示信息与生物标志物评分图。基于文献构建了六项生物标志物：语法异常、语用障碍、命名困难、对话轮换中断、发音含糊和语调变化，利用两个数据集（DementiaBank和Indiana）进行开发。还构建了一个融合六项指标的综合评分。在DementiaBank数据集上，综合评分与MMSE评分呈现中度相关性，优于单一指标。Indiana数据集分析显示标志物得分更高且波动更大，可能与人群差异（如痴呆严重程度）及对话场景（人-机对话不同于人-人对话）有关。研究强调需进一步探究对话场景对生物标志物的影响及其临床应用潜力。
原文摘要 · Abstract (English)
This study presents the development and testing of a conversational speech system designed for robots to detect speech biomarkers indicative of cognitive impairments in people living with dementia (PLwD). The system integrates a backend Python WebSocket server and a central core module with a large language model (LLM) fine-tuned for dementia to process user input and generate robotic conversation responses in real-time in less than 1.5 seconds. The frontend user interface, a Progressive Web App (PWA), displays information and biomarker score graphs on a smartphone in real-time to human users (PLwD, caregivers, clinicians). Six speech biomarkers based on the existing literature - Altered Grammar, Pragmatic Impairments, Anomia, Disrupted Turn-Taking, Slurred Pronunciation, and Prosody Changes - were developed for the robot conversation system using two datasets, one that included conversations of PLwD with a human clinician (DementiaBank dataset) and one that included conversations of PLwD with a robot (Indiana dataset). We also created a composite speech biomarker that combined all six individual biomarkers into a single score. The speech system's performance was first evaluated on the DementiaBank dataset showing moderate correlation with MMSE scores, with the composite biomarker score outperforming individual biomarkers. Analysis of the Indiana dataset revealed higher and more variable biomarker scores, suggesting potential differences due to study populations (e.g. severity of dementia) and the conversational scenario (human-robot conversations are different from human-human). The findings underscore the need for further research on the impact of conversational scenarios on speech biomarkers and the potential clinical applications of robotic speech systems.
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