人形机器人让用户说话更流畅、语法更复杂。
Does the Appearance of Autonomous Conversational Robots Affect User Spoken Behaviors in Real-World Conference Interactions?
- 对比人形与非人形机器人，分析对话中的语言特征。
- 与人形机器人对话时，用户错语减少，句式更复杂。
- 适合关注人机交互设计与自然语言行为的研究者。
我们通过对比人形机器人 ERICA 与非人形机器人 TELECO，研究机器人外观对真实世界会议中用户口语行为的影响。基于 SIGDIAL 2024 上 42 名参与者的数据，从对话文本中提取了停顿、错语和句法复杂度等语言特征。结果显示，与 TELECO 相比，用户在与 ERICA 对话时错语更少，句法更复杂，效应量中等。进一步训练的朴素贝叶斯分类模型达到 71.60% 的 F1 得分，并通过特征重要性分析确认错语和句法复杂度是关键影响因素。结合认知负荷与沟通适应理论，我们认为优化机器人外观以引导用户更流畅、结构化的表达，有助于提升人机沟通协调性。
原文摘要 · Abstract (English)
We investigate the impact of robot appearance on users' spoken behavior during real-world interactions by comparing a human-like android, ERICA, with a less anthropomorphic humanoid, TELECO. Analyzing data from 42 participants at SIGDIAL 2024, we extracted linguistic features such as disfluencies and syntactic complexity from conversation transcripts. The results showed moderate effect sizes, suggesting that participants produced fewer disfluencies and employed more complex syntax when interacting with ERICA. Further analysis involving training classification models like Naïve Bayes, which achieved an F1-score of 71.60\%, and conducting feature importance analysis, highlighted the significant role of disfluencies and syntactic complexity in interactions with robots of varying human-like appearances. Discussing these findings within the frameworks of cognitive load and Communication Accommodation Theory, we conclude that designing robots to elicit more structured and fluent user speech can enhance their communicative alignment with humans.
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