使用变压器进行跨体语音情绪识别:利用手工制作的功能和数据增强
1Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.
Computers in biology and medicine
|July 13, 2024
概括
这项研究引入了一种新的语音情感识别 (SER) 框架,通过使用高级特征提取和跨体模型来提高复杂场景的准确性. 新方法在多个数据集上显著优于现有方法.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 语音处理 语音处理
背景情况:
- 语音情感识别 (SER) 对于心理评估和人机交互等应用至关重要.
- 使用手工工程功能的传统SER方法与复杂的数据变化,噪音和跨数据集不一致性作斗争.
- 现有的技术在多样化和具有挑战性的声学环境中经常显示分类精度降低.
研究的目的:
- 开发和评估一种新的SER框架,以提高情绪分类的准确性.
- 解决手动功能工程在处理各种语音模式和噪音数据方面的局限性.
- 创建一个强大的跨体模型,在不同的数据集中增强SER性能.
主要方法:
- 应用信号预处理和数据增强以增强原始语音信号.
- 导出了18个信息特征,并为歧视性特征集采用了特征选择.
- 使用SAVEE,RAVDESS和EMO-DB数据集进行培训和测试,包括跨体模型.
主要成果:
- 与现有方法相比,拟议的SER框架实现了更高的性能.
- 获得了很高的准确率:SAVEE的95%,RAVDESS的94%,EMO-DB的97%和跨体模型的97%.
- 该框架在处理各种情绪表达和数据复杂性方面表现出有效性.
结论:
- 新的SER框架显著提升了语音情感识别的最新技术.
- 预处理,增强和功能选择的综合方法产生了强大的性能.
- 该研究强调了拟议模型在现实世界SER应用中的潜力.
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