对多模态情绪识别野生数据集的评估
Ana Aguilera1, Diego Mellado2, Felipe Rojas1
1Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2340000, Chile.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
对多式联动情绪识别的野生数据集的评估揭示了局限性. 建议将多个数据集结合起来,以便在情感AI系统中获得更好的现实世界性能.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 情感计算是一种情感计算.
背景情况:
- 多模式情绪识别利用各种数据 (面孔,语音,文本) 来识别人类的情绪.
- 当前的深度学习模型通常在现实环境中扎,原因是对受控数据集的训练.
研究的目的:
- 评估用于多式联动情绪识别的野生数据集的优缺点.
- 评估现有数据集对于实际情感AI应用的适用性.
主要方法:
- 评估了四个野生动物数据集:AFEW,SFEW,MELD和AffWild2.
- 使用预先设计的多式联网架构进行性能评估.
- 使用精度和F1-Score指标测量性能.
主要成果:
- 野生数据集对独立的多式联络式情绪识别有局限性.
- 数据集的适用性通常受其原始设计目的 (例如,面部或语音识别) 的限制.
结论:
- 单独的野生数据集不足以进行强大的多式联络情绪识别.
- 结合多个数据集和平衡类样本对于改善现实世界的性能至关重要.
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