基于轨道局部二进制模式和适当的子频段选择技术的远程教育系统的自动语音情感两极化
Dahiru Tanko1, Fahrettin Burak Demir2, Sengul Dogan1
1Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
概括
这项研究开发了一种自动语音情感识别 (SER) 模型,以评估讲师在远程教育中的表现. 新型模型在新数据集上实现了93.40%的准确性,证明了其有效性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 教育 技术 技术 教育 技术
背景情况:
- 随着COVID-19的流行,远程教育系统的采用加速了.
- 评估讲师的表现对于确保远程学习的有效性至关重要.
- 自动语音情感识别 (SER) 为客观绩效评估提供了一个潜在的解决方案.
研究的目的:
- 开发一个准确的SER模型来评估在线教学期间讲师的情绪状态.
- 创建和利用一个新的语音情感数据集来训练和测试SER模型.
主要方法:
- 使用多级别离散波形变换 (DWT) 和1D轨道局部二进制模式 (1D-OLBP) 的特征提取.
- 使用邻近组件分析 (NCA) 进行特征选择.
- 使用支持矢量机 (SVM) 进行分类,并进行十倍的交叉验证.
主要成果:
- 拟议的1D-OLBP和NCA的SER模型在新收集的7101个声音段的数据集上实现了93.40%的分类准确性,跨越三个情绪状态.
- 该模型证明了可通用性,在三个公共SER数据集上实现了超过70%的准确性.
结论:
- 开发的SER模型对于在远程教育环境中分析讲师情绪是有效的.
- 这种新的方法显示出对在线环境中客观评估教学有效性的希望.
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