一个丰富的注释数据集的共同语音手势跨越不同的扬声器环境
Laura B Hensel1, Stephanie Cheng2, Stacy Marsella2,3
1University of Glasgow, School of Psychology and Neuroscience, Glasgow, G12 8QB, United Kingdom. laura.hensel@glasgow.ac.uk.
Scientific data
|November 5, 2025
概括
这项研究引入了来自不同演讲者的2373个注释手势的新数据集,有助于人类沟通和AI开发的研究. 数据包括详细的手势属性和3D姿势,扩大了手势词典.
科学领域:
- 语言学的语言学.
- 计算机科学 计算机科学
- 人与计算机的交互
背景情况:
- 人类沟通严重依赖于复杂的手势,这给分析和AI建模带来了挑战.
- 现有的手势数据集可能缺乏深入研究所需的全面注释和多式联络数据.
研究的目的:
- 呈现一个新的,大规模的手势注释数据集,以推进人类沟通的研究.
- 为训练人工智能模型提供丰富的资源,用于手势识别和生成.
- 支持开发能够理解和产生手势的社会智能虚拟代理.
主要方法:
- 收集了来自三个专业类别的9名演讲者的2373个注释手势:大学讲师,政治家和心理治疗师.
- 标注的手势与类型 (例如,隐喻,标志性),描述性术语,以及相关的口头发言.
- 包括详细的物理属性 (例如,手的高度,速度) 和3D姿势跟踪数据,并在可用的情况下补充视频记录.
主要成果:
- 数据集包括2373个有广泛注释的手势,包括物理属性和3D姿势数据.
- 确定了几种以前未经记录的手势词汇,为手势研究提供了新的见解.
- 数据的多模式性质 (视频,3D跟踪,注释) 为分析提供了丰富的背景.
结论:
- 本文所介绍的手势数据集是人类沟通和人工智能研究人员的宝贵资源.
- 促进了手势识别,生成和创建更复杂的虚拟代理的进步.
- 通过详细的注释和新发现,扩大了对非语言沟通的当前理解和词汇.
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