用纸牌游戏收集法语多模态情绪数据，支持面部、语音和手势分析。
CG-MER: A Card Game-based Multimodal dataset for Emotion Recognition
- 通过纸牌游戏互动收集情绪表达数据
- 涵盖10场会话、20名参与者，含面部/语音/手势三模态
- 适合情感计算与人机交互研究者使用
情感计算领域在探索情绪与新兴技术关系方面取得了显著进展。本文提出一个全新的法语多模态情绪识别数据集——CG-MER，包含面部表情、语音和手势三种主要模态，提供对情绪的全面视角。该数据集还可扩展至自然语言处理（NLP）等其他模态。数据通过10场纸牌游戏会话采集，参与人数为20人（9名女性，11名男性），参与者在回应多样问题时被要求表达不同情绪。该数据集为情绪识别研究提供了宝贵资源，有助于深入探索人类情绪与数字技术间的复杂关联。
原文摘要 · Abstract (English)
The field of affective computing has seen significant advancements in exploring the relationship between emotions and emerging technologies. This paper presents a novel and valuable contribution to this field with the introduction of a comprehensive French multimodal dataset designed specifically for emotion recognition. The dataset encompasses three primary modalities: facial expressions, speech, and gestures, providing a holistic perspective on emotions. Moreover, the dataset has the potential to incorporate additional modalities, such as Natural Language Processing (NLP) to expand the scope of emotion recognition research. The dataset was curated through engaging participants in card game sessions, where they were prompted to express a range of emotions while responding to diverse questions. The study included 10 sessions with 20 participants (9 females and 11 males). The dataset serves as a valuable resource for furthering research in emotion recognition and provides an avenue for exploring the intricate connections between human emotions and digital technologies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。