妇女与情感多模式情感计算数据集
Jose A Miranda Calero1,2, Laura Gutiérrez-Martín3, Esther Rituerto-González4,5
1Embedded Systems Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Vaud, Switzerland. jose.mirandacalero@epfl.ch.
Scientific data
|October 31, 2024
概括
该数据集捕获了来自100名妇女的生理和语音数据,以检测基于性别的暴力. 技术验证证实数据集有效地引起和记录情绪,有助于暴力检测研究.
科学领域:
- 多模式情绪识别多模式情绪识别
- 生理信号处理 物理信号处理
- 语音分析用于情绪检测和检测情绪.
背景情况:
- 基于性别的暴力 (GBV) 检测需要细微的情感理解.
- 现有的数据集可能缺乏多模式情感数据,特别是来自不同人群的情感数据.
- 虚拟现实 (VR) 提供了一个可控的情绪诱导环境.
研究的目的:
- 介绍WEMAC数据集,这是一个开放的,用于GBV检测的多模式资源.
- 在100名女性中,与生理和语音反应相关联引起的情绪.
- 验证数据集在捕捉与恐惧相关的情绪方面的有效性,这对于GBV检测至关重要.
主要方法:
- 收集了100名女性的生理数据 (血液体积脉冲,皮肤电动反应,皮肤温度) 和语音录音.
- 使用通过VR耳机观看的验证视频引起的情绪.
- 实验对象使用分类和维度尺度 (自我评估人形) 自我报告的情绪.
主要成果:
- 技术验证显示,引起的和报告的分类情绪之间存在强烈的正相关性.
- 在恐惧和非恐惧情绪报告之间观察到显著的负相关性.
- 该数据集成功地引起了与GBV检测相关的目标情绪,特别是恐惧.
结论:
- WEMAC数据集是多模态情感研究的验证资源,特别是用于GBV检测.
- 数据有效地捕捉了引起情绪的生理和语音相关性.
- 数据集的焦点是恐惧检测,为推进GBV研究提供了有价值的工具.
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