在使用同行基于语音的双谱特征和影响动态的对话中识别情绪气候
概括
这项研究介绍了MLBspeak,这是一种用于识别对话中的情绪气候的AI方法. 它准确地识别了共同的情绪,提供了对社会互动和临床评估的见解.
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:
- 在使用人工智能的对话中识别情绪对于理解社会行为至关重要.
- 目前的方法缺乏捕捉对话的动态,共同的情感氛围的能力.
- 认识情绪气候 (EC) 对于更深入地了解人际关系动态至关重要.
研究的目的:
- 提出和评估MLBispeC (基于机器学习的双光谱分类),一种基于AI的新方法,用于识别对话中的情绪气候 (EC).
- 扩展情感识别超越个人影响到集体情感氛围.
- 为了证明双光谱分析和机器学习对EC认可的有效性.
主要方法:
- 利用时间窗口对语音信号的双光谱分析,从非线性波相互作用中提取特征.
- 结合从情感标签中提取的特征与影响动态,创建一个扩展的特征向量.
- 员工支持矢量机和K-最近邻居分类器用于分类情绪气候.
主要成果:
- 与IEMOCAP数据集上最先进的方法相比,MLBispeC取得了更高的性能.
- 报告了高精度 (0.826A/0.754V),灵敏度 (0.864A/0.774V) 和AUC (0.821A/0.799V) 的兴奋和价值维度.
- 在对话中表现出客观和动态地识别同伴的情绪气候.
结论:
- MLBispeC有效地识别了对话中的情感气候,为情感和社会互动提供了客观的见解.
- 该方法具有动态评估EC的能力,可以帮助评估患有各种生理,心理和精神健康状况的患者.
- 这种方法为不同年龄段的低调,客观和动态评估提供了有价值的工具.
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