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机器人应该显示他们听到的东西吗? 听力障碍作为一个实际的成就

Damien Rudaz¹, Christian Licoppe²

¹Department of Nordic Studies and Linguistics, University of Copenhagen, Copenhagen, Denmark.

Frontiers in robotics and AI

|October 13, 2025

概括

显示机器人的自动语音识别 (ASR) 结果会影响人类如何感知错误通信. 这种透明度可以重新定义人机交互 (HRI) 中的相互理解,突出潜在的设计后果.

科学领域:

人与机器人的交互 (HRI)
人工智能中的透明度
沟通研究沟通研究

背景情况:

了解人机交互 (HRI) 中的故障对于设计有效的对话代理至关重要.
透明度的作用,特别是显示自动语音识别 (ASR) 结果,在塑造交互动态方面是未被充分探索的.

研究的目的:

调查公开显示机器人的ASR结果是否会影响HRI中错误通信的出现和管理.
分析ASR转录创建的"信息生态"如何影响参与者对机器人行为的解释.

主要方法:

在人机交互实验中对参与者的凝视行为进行初步定量分析.
微分析方法检查交互序列和机器人发言和手势的解释.
案例研究重点关注ASR转录改变了参与者对互动成功的看法.

主要成果:

显示ASR转录显著影响了参与者对机器人行动的解释,认为机器人行为不够或"失败".
只有在参与者阅读ASR成绩单后,错误沟通的情况才被重新评估为有问题的.
由ASR显示器创建的信息生态修改了"相互理解"的定义,以参与者为导向.

结论:

系统地显示ASR转录在参与者如何在HRI中解释共同构建的行动方面发挥着关键作用.
"错误"和"错误"是互动的成就,而不是预先定义的技术或社会问题.
显示ASR转录的设计决定需要仔细考虑,因为它可能对人类-AI通信产生意想不到的后果.

关键词:

行动属性归因行为.自动语音识别自动语音识别谈话分析会话分析错误和错误的错误和错误的错误.民族方法学民族方法学听力问题听力问题维修维修维修维修维修透明度透明度透明度

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