| 摘要: |
| 目的 通过自上而下的认知建模方法进行人机语音交互建模，实现人的绩效和满意度预测，为智能语音交互系统的设计方案的评估提供新思路和新方法。方法 基于人的信息处理排队网络模型(QN-MHP)，结合人机语音交互过程的理论研究，建立人机语音交互过程中人的绩效和满意度预测模型，以预测不同语音交互系统的识别情况(识别自然语言、识别限制语言)、语音交互系统的激活方法(唤醒激活、按一次/点击激活、按住说、直接说)、语音交互系统的平均识别准确率(连续变量)和语音交互系统的平均识别延迟(连续变量)下人使用语音交互系统完成任务的时间和用户满意度。结论 本文基于人的信息处理排队网络模型(QN-MHP)建立了不同语音交互系统的设计参数下的人的绩效和满意度预测模型，将本模型应用于家居场景中，能用于评估不同的家居服务机器人的语音交互系统的设计方案的有效性。本研究建立的语音交互过程中人的绩效和满意度预测模型能为设计师和工程师提供便捷有效的智能语音交互系统的评估工具，从而为不同需求下的智能语音系统的设计、优化和应用提供参考。 |
| 关键词: 人机语音交互 人的信息排队网络模型 人的认知与仿真建模 任务完成时间 用户满意度 |
| DOI：10.19554/j.cnki.1001-3563.2025.04.019 |
| 分类号: |
| 基金项目:国家自然科学基金委重大项目总项目(7219282;72192824) |
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| Human Cognition and Simulation Modeling and its Application in Designing Voice Interaction in Home Intelligent Devices |
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ZHANG Wei, WU Changxu
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(Tsinghua University, Beijing 100084, China)
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| Abstract: |
| This study aims to model a human-machine voice interaction process by top-down modeling methods and predict human performance and satisfaction during voice interaction. The present work provides new insights and methods for evaluating the effectiveness of different designs of voice interactive systems. Based on Queuing Network-Model Human Processor (QN-MHP) and voice interaction theories, the present research developed models to predict human voice-interaction completion time and user satisfaction under different speak recognition conditions (i.e., recognize natural language and recognize restricted language), system activation methods (i.e., wakeup, click-to-activate, hold-to-talk, and direct-talk), the average voice recognition accuracy (continuous variables), and the average voice recognition delays (continuous variables). In conclusion, based on QN-MHP, models were developed to predict human task completion time and user experiences considering different designing parameters. As an example, these models were applied in home environments to evaluate the effectiveness of different designs of the voice-interactive system for a home service robot. The models proposed by this research can provide designers and engineers with a useful tool for designing, optimizing, and applying intelligent voice-interactive systems under different requirements. |
| Key words: human-machine voice interactions Queuing Network-Model Human Processor (QN-MHP) human cognition and simulation modeling task completion time user satisfaction |
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