While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, resulting in unexpected changes in the flow of conversation. We analyzed how such changes are linked with learning gains and learners' rapport with the agents. Our results show they are not related to learning gains or rapport, regardless of the types of responses the agents should have returned given the correct input from learners without ASR errors. We also discuss the implications for optimal error-recovery policies for teachable agents that can be drawn from these findings.

翻译：尽管具备语音功能的教学智能体相比基于文本输入的智能体具有某些优势，但它们容易因自动语音识别（ASR）的误认错误而受到影响。这些错误可能进一步传递，导致对话流程出现意外变化。我们分析了这些变化如何与学习效果及学习者和智能体之间的人际关系相关联。结果表明，无论教学智能体在无ASR错误情况下应基于学习者的正确输入返回何种类型的回应，这些变化均与学习效果或人际关系无关。我们还讨论了这些发现对教学智能体最优错误恢复策略的启示。