HiMul-LGG:基于层次决策融合的局部-全球图形神经网络,用于对话中的多式联动情绪识别
Changzeng Fu1, Fengkui Qian2, Kaifeng Su2
1Northeastern University, China; Osaka University, Japan; RIKEN, Japan; Hebei Key Laboratory of Marine Perception Network and Data Processing, China.
概括
本研究介绍了HiMul-LGG,这是一种用于对话情绪识别 (ERC) 的新方法,可以有效处理单模差异和扬声器依赖. 层次决策融合策略增强了多式联运特征对齐和性能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 对话情绪识别 (ERC) 对于理解人类互动至关重要.
- 现有的方法往往侧重于多模式融合,忽视单模式表示问题和扬声器依赖性.
研究的目的:
- 为多式联络ERC提出一种基于层次决策融合的新型局部-全球图形神经网络 (HiMul-LGG).
- 在情感识别中解决单模表示差异和扬声器依赖性的问题.
主要方法:
- 实施了层次决策融合策略,以实现跨模式特征对齐.
- 使用了局部-全球图形神经网络架构来捕获模式间和模式内扬声器依赖.
- 采用跨模式多头注意力机制来增强跨模式互动.
主要成果:
- 与IEMOCAP和MELD数据集上的现有方法相比,HiMul-LGG表现出更高的性能.
- 废除研究证实了等级决策融合和局部-全球图形构造的有效性.
结论:
- 拟议的HiMul-LGG有效地解决了当前ERC模型中的局限性.
- 层次融合和基于图形的方法通过考虑复杂的依赖关系,显著提高了情绪识别准确性.
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