寻找有效的预处理方法和基于CNN的架构,以有效的道关注语音情感识别和语音情感识别
Byunggun Kim1, Younghun Kwon2,3
1Department of Applied Artificial Intelligence, Hanyang University(ERICA), Ansan, 425-791, Kyunggi-Do, Republic of Korea.
Scientific reports
|September 24, 2025
概括
这项研究使用卷积神经网络 (CNN) 增强了语音情感识别 (SER). 通过优化短期里埃转换 (STFT) 预处理和整合高效通道注意力 (ECA),拟议的模型实现了卓越的性能.
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) 性能通过深度学习得到了改善.
- 使用光谱图的卷积神经网络 (CNN) 模型对于SER很受欢迎.
- 对于SER的最佳预处理和CNN架构仍然不清楚.
研究的目的:
- 调查SER的有效预处理方法和CNN架构.
- 在SER模型中增强情感特征分辨率和道过器有效性.
主要方法:
- 为SER准备了八个数据集,具有不同的频率-时间分辨率.
- 建议使用不同窗口大小的短期里埃变换 (STFT) 数据增强.
- 设计了CNN架构,包括带有高效频道注意力 (ECA) 块的6层CNN.
主要成果:
- 在预处理中增加频率分辨率,改善情绪识别.
- 有两个ECA块的CNN模型超过了以前的SER模型.
- 采用STFT数据增强的拟议模型实现了最高的SER性能.
结论:
- 优化的STFT预处理和ECA集成对SER有效.
- 拟议的基于CNN的方法在语音情感识别方面取得了重大进展.
- 进一步的研究可以探索先进的注意力机制,以改善SER.
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