High-quality parent-child conversational interactions are crucial for children's social, emotional, and cognitive development. However, many children have limited exposure to these interactions at home. As increasingly accessible and scalable interventions in child development, interactive technologies, such as social robots, have great potential for facilitating parent-child interactions. However, such technology-based interventions are still underexplored, as the technologies’ limited ability to understand the social-emotional dynamics of human dyadic interactions impedes their effective delivery of timely, adaptive interventions. To advance research on resolving this roadblock, we present a “dyadic affect in multimodal interaction - parent to child” (DAMI-P2C) dataset collected during a study of 34 parent-child pairs, where parents and children (3-7 years old) engaged in reading storybooks together. In contrast to existing public datasets for social-emotional behaviors in dyadic interactions, each instance for both participants in our dataset was annotated for affect by three labelers. Additionally, the dataset contains audiovisual recordings as well as each dyad's sociodemographic profiles, co-reading behaviors, affect labels, and body joints. We describe the dataset's main characteristics and provide a preliminary analysis of the interrelations between sociodemographic profiles, co-reading behaviors, and affect labels. The dataset provides us with useful insights into the computing and social science fields.

中文翻译：

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亲子多模态互动中的二元情感：DAMI-P2C数据集介绍及其初步分析


高质量的亲子对话互动对于孩子的社交、情感和认知发展至关重要。然而，许多孩子在家里接触这些互动的机会有限。随着儿童发展干预措施越来越容易获得和扩展，社交机器人等互动技术在促进亲子互动方面具有巨大潜力。然而，这种基于技术的干预措施仍未得到充分探索，因为技术理解人类二元互动的社会情感动态的能力有限，阻碍了它们有效地提供及时、适应性的干预措施。为了推进解决这一障碍的研究，我们提出了“多模式互动中的二元影响 - 父母对孩子”(DAMI-P2C) 数据集，该数据集是在对 34 个亲子对的研究中收集的，其中父母和孩子（3-7 岁）一起阅读故事书。与二元交互中的社交情感行为的现有公共数据集相比，我们数据集中两个参与者的每个实例都由三个标记者注释了影响。此外，该数据集还包含视听记录以及每个二人的社会人口统计资料、共同阅读行为、情感标签和身体关节。我们描述了数据集的主要特征，并对社会人口概况、共同阅读行为和情感标签之间的相互关系进行了初步分析。该数据集为我们提供了对计算和社会科学领域的有用见解。