使用双功能提取编码器增强语音情感识别
Ilkhomjon Pulatov1, Rashid Oteniyazov2, Fazliddin Makhmudov1
1Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一个新的语音情感识别框架,使用谱图和语义特征. 该系统实现了高精度,在基准数据集上表现优于现有模型.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 语音处理 语音处理
背景情况:
- 准确的语音情感识别对于人机交互至关重要.
- 现有的方法在性能和精度上有局限性.
- 从语言中开发强大的情绪检测是一个持续的挑战.
研究的目的:
- 开发用于语音情感识别 (SER) 的创新框架.
- 通过解决当前方法学的不足来提高SER性能.
- 为了提高人类语言情感解释的准确性和有效性.
主要方法:
- 使用完全卷积神经网络进行语音谱图转录.
- 采用Mel-frequency cepstral coefficient (MFCC) 功能提取,与语音2Vec集成用于语义编码.
- 通过长期短期存储器 (LSTM) 网络和完全连接的层处理双特性.
主要成果:
- 在RAVDESS数据集上获得了94.8%的准确性.
- 在EMO-DB数据集上实现了94.0%的准确性.
- 与已建立的SER模型相比,表现出卓越的性能.
结论:
- 拟议的框架显著提高了语音情感识别准确度.
- 谱图和语义特征的结合方法被证明是有效的.
- 该系统为SER提供了更复杂,更有效的解决方案.
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