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简介:MemoCMT:使用基于交叉模式变压器的特征融合功能进行多模式情感识别
Mustaqeem Khan1, Phuong-Nam Tran2, Nhat Truong Pham3
1College of Information Technology, United Arab Emirates University (UAEU), Al Ain, Abu Dhabi, United Arab Emirates, 5551, Al Ain, UAE.
MemoCMT通过使用新的交叉模式变压器集成音频和文本功能来增强语音情感识别. 这种高效的系统在基准数据集上实现了高精度,对现实世界的应用有很大的希望.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 变压器模型在语音情感识别方面表现出色,但在计算上昂贵.
- 卷积神经网络更快,但在捕捉远程语音模式方面受到限制.
- 现有的方法面临着平衡准确性和计算效率的挑战.
研究的目的:
- 开发一个高效准确的语音情感识别系统.
- 引入一种新的交叉模式变压器 (CMT) 用于分析本地和全球语音特征与文本.
- 为了利用像HUBERT和BERT这样的预训练模型来提取特征.
主要方法:
- 建议使用交叉模式变压器 (CMT) 的MemoCMT系统.
- 集成HuBERT用于音频特征提取和BERT用于文本分析.
- 应用各种融合技术后特征集成的情绪分类.
主要成果:
- 在IEMOCAP和ESD基准公司中,MemoCMT取得了高绩效.
- 使用最小聚合的CMT产生了最高的未加权精度 (UW-Acc),分别为81.33%和91.93%.
- 权重准确度 (W-Acc) 分别达到81.85%和91.84%.
结论:
- MemoCMT表现出强大的概括能力和稳定性.
- 该系统为语音情感识别提供了有效的解决方案.
- 公共可用的实施促进了可复制性和进一步的研究.
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