实时估算语音工作负荷，让人机系统自适应调整交互方式。
Robust Speech-Workload Estimation for Intelligent Human-Robot Systems
- 基于语音信号实时分析，分解出说话时的认知与生理负荷
- 在不同人员和人机协作模式下均保持高估准率，跨人群泛化性强
- 为智能机器人系统动态调节任务难度与交互方式提供关键支持
高要求任务环境（如远程操控无人机）需要操作者快速准确完成任务，但低或高工作负荷状态会降低表现。通过实时感知操作者负荷状态并动态调节系统需求与交互方式，可提升整体性能。该系统需实时估计各项负荷成分（认知、身体、视觉、语音、听觉）。现有方法多为事后分析，且极少支持实时语音负荷估计。本文提出一种实时语音工作负荷估计算法，并验证其准确性及在不同个体和人机协同模式下的通用性。实证结果表明，该算法具备强泛化能力，是构建自适应人机系统的必要基础。
原文摘要 · Abstract (English)
Demanding task environments (e.g., supervising a remotely piloted aircraft) require performing tasks quickly and accurately; however, periods of low and high operator workload can decrease task performance. Intelligent modulation of the system's demands and interaction modality in response to changes in operator workload state may increase performance by avoiding undesirable workload states. This system requires real-time estimation of each workload component (i.e., cognitive, physical, visual, speech, and auditory) to adapt the correct modality. Existing workload systems estimate multiple workload components post-hoc, but few estimate speech workload, or function in real-time. An algorithm to estimate speech workload and mitigate undesirable workload states in real-time is presented. An analysis of the algorithm's accuracy is presented, along with the results demonstrating the algorithm's generalizability across individuals and human-machine teaming paradigms. Real-time speech workload estimation is a crucial element towards developing adaptive human-machine systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。