Evaluating data-driven co-speech gestures of embodied conversational agents through real-time interaction

Yuan he, André Pereira, Taras Kucherenko
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引用次数: 4

Abstract

Embodied Conversational Agents (ECAs) that make use of co-speech gestures can enhance human-machine interactions in many ways. In recent years, data-driven gesture generation approaches for ECAs have attracted considerable research attention, and related methods have continuously improved. Real-time interaction is typically used when researchers evaluate ECA systems that generate rule-based gestures. However, when evaluating the performance of ECAs based on data-driven methods, participants are often required only to watch pre-recorded videos, which cannot provide adequate information about what a person perceives during the interaction. To address this limitation, we explored use of real-time interaction to assess data-driven gesturing ECAs. We provided a testbed framework, and investigated whether gestures could affect human perception of ECAs in the dimensions of human-likeness, animacy, perceived intelligence, and focused attention. Our user study required participants to interact with two ECAs - one with and one without hand gestures. We collected subjective data from the participants' self-report questionnaires and objective data from a gaze tracker. To our knowledge, the current study represents the first attempt to evaluate data-driven gesturing ECAs through real-time interaction and the first experiment using gaze-tracking to examine the effect of ECAs' gestures.
通过实时交互评估嵌入会话代理的数据驱动的协同语音手势
嵌入会话代理(eca)利用协同语音手势可以在许多方面增强人机交互。近年来,数据驱动的eca手势生成方法引起了广泛的研究关注,相关方法也在不断完善。实时交互通常用于研究人员评估产生基于规则的手势的ECA系统。然而,当基于数据驱动的方法评估eca的性能时,参与者通常只需要观看预先录制的视频,这些视频无法提供关于一个人在交互过程中所感知到的足够信息。为了解决这一限制,我们探索了使用实时交互来评估数据驱动的手势eca。我们提供了一个测试平台框架,并研究了手势是否会影响人类对eca的感知,包括人类的相似性、动物性、感知智力和集中注意力。我们的用户研究要求参与者与两个eca互动——一个有手势,一个没有手势。我们从参与者的自我报告问卷中收集主观数据,从凝视追踪器中收集客观数据。据我们所知,目前的研究是第一次尝试通过实时交互来评估数据驱动的手势eca,也是第一次使用视线跟踪来检查eca手势效果的实验。
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