Integrating Representational Gestures into Automatically Generated Embodied Explanations and its Effects on Understanding and Interaction Quality

TL;DR

This paper explores how integrating different types of gestures into virtual agents affects explanation quality and understanding, finding that embodied agents improve comprehension even if specific gestures do not outperform baseline conditions.

Contribution

It introduces a model combining speech-driven beat gestures with manually captured iconic gestures in virtual explanations, assessing their impact on understanding and interaction.

Findings

Embodied agents significantly improve understanding over non-embodied ones.

Iconic gestures alone do not outperform beat gestures or baseline conditions.

Gesture integration enhances interaction quality despite mixed effects on understanding.

Abstract

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.