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音频,视觉和文本模式的融合使用交叉模式注意力来识别情绪
Avishek Das1, Moumita Sen Sarma1, Mohammed Moshiul Hoque1
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
研究人员开发了一个新的多式联络孟加拉语数据集和情感识别框架,通过集成音频,视频和文本数据来提高准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 多模态情感分类 (MEC) 集成音频,视频和文本,用于强大的情感识别.
- 挑战包括融合多种数据模式和缺乏孟加拉语特定数据集.
- 现有的系统在细微的情感表达中扎.
研究的目的:
- 介绍MAViT-孟加拉语数据集,这是孟加拉语情感识别的新型多式联络资源.
- 开发和评估一个跨模式的注意力框架 (AVaTER) 加强MEC.
- 在孟加拉语情感分析中解决单模式方法的局限性.
主要方法:
- 创建了MAViT-Bangla数据集,包含1002个音频,视频和文本样本,涵盖愤怒,恐惧,快乐和悲伤.
- 开发了AVaTER框架,利用跨模式关注特征融合.
- 评估了框架的表现与单模式方法相比.
主要成果:
- 孟加拉MAViT数据集为孟加拉MEC研究提供了一个全面的资源.
- 在AVaTER框架中,F1得分为0.64.
- 这比单模式情绪识别技术有了显著的改进.
结论:
- 孟加拉语MAViT数据集是孟加拉语多式联络情感识别研究的宝贵贡献.
- AVaTER框架有效地整合了多式联络功能,以提高情绪分类的准确性.
- 未来的工作可以利用这个数据集和框架来更复杂的孟加拉语情感理解.
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