In human interaction, gestures serve various functions such as marking speech rhythm, highlighting key elements, and supplementing information. These gestures are also observed in explanatory contexts. However, the impact of gestures on explanations provided by virtual agents remains underexplored. A user study was carried out to investigate how different types of gestures influence perceived interaction quality and listener understanding. This study addresses the effect of gestures in explanation by developing an embodied virtual explainer integrating both beat gestures and iconic gestures to enhance its automatically generated verbal explanations. Our model combines beat gestures generated by a learned speech-driven synthesis module with manually captured iconic gestures, supporting the agent's verbal expressions about the board game Quarto! as an explanation scenario. Findings indicate that neither the use of iconic gestures alone nor their combination with beat gestures outperforms the baseline or beat-only conditions in terms of understanding. Nonetheless, compared to prior research, the embodied agent significantly enhances understanding.

翻译：在人际互动中，手势具有多种功能，例如标记言语节奏、突出关键要素以及补充信息。这些手势在解释性语境中同样被观察到。然而，手势对虚拟智能体所提供解释的影响仍未得到充分探索。本研究进行了一项用户调查，以探究不同类型的手势如何影响感知的交互质量和听者理解。该研究通过开发一个整合了节拍手势和图标手势的具身虚拟解释器来增强其自动生成的口头解释，从而探讨手势在解释中的作用。我们的模型结合了由学习到的语音驱动合成模块生成的节拍手势与手动捕捉的图标手势，以支持智能体关于棋盘游戏Quarto!的口头表达作为解释场景。研究结果表明，无论是单独使用图标手势还是将其与节拍手势结合使用，在理解方面均未优于基线条件或仅使用节拍手势的条件。尽管如此，与先前研究相比，该具身智能体显著提升了理解效果。