一个深度的神经模糊框架用于语音情绪识别和表达
1Information Technology and Cultural Management Institute, Hebei Institute of Communications, Shijiazhuang051430, Hebei, China.
Computer methods in biomechanics and biomedical engineering
|September 17, 2025
概括
这项研究引入了一种新的深度神经模糊框架,用于准确的语音情感识别 (SER). 拟议的方法显著提高了情绪检测的准确性,优于现有的模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 语音情感识别 (SER) 对人机交互,医疗保健和教育至关重要.
- 像自适应神经模糊推理系统 (ANFIS) 和深度神经网络 (DNN) 这样的现有方法在处理高维语音数据和解释性方面存在局限性.
- 需要强大的SER技术来准确检测情绪.
研究的目的:
- 提出一种混合深度神经模糊框架,用于增强语音情感识别 (SER).
- 结合深度神经网络 (DNN) 的优势来进行特征提取和适应性神经模糊推理系统 (ANFIS) 的优势来进行分类.
- 解决ANFIS在高维数据和DNN缺乏解释性的局限性.
主要方法:
- 开发了一个集成DNN和ANFIS的新框架.
- 该框架包括模糊化,深度特征提取和模糊化单元.
- 拟议的模型在三个标准语音情感数据库上进行了评估:RML,SAVEEE和RAVDESS.
主要成果:
- 深度神经模糊框架在SER中实现了高精度,达到高达97.95%.
- 与独立的ANFIS,DNN和预训练模型相比,拟议的模型表现出更高的性能.
- 该框架有效地处理高维语音数据,并增强可解释性.
结论:
- 提出的深度神经模糊框架为语音情感识别提供了强大而准确的解决方案.
- 这种混合方法显示了推动SER研究和应用的巨大潜力.
- 该框架的有效性在多个基准语音数据集中得到验证.
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