DSTCNet:用于语音情感识别的深层光谱时间通道注意网络
IEEE transactions on neural networks and learning systems
|August 25, 2023
概括
这项研究引入了一种新的深层光谱时空通道网络 (DSTCNet),用于语音情感识别 (SER). DSTCNet通过结合注意力机制来增强传统的卷积神经网络 (CNN),以更好地捕捉语言中的情感线索.
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) 对于改善人机交互至关重要.
- 目前的深度学习方法,特别是CNN,被广泛使用,但在建模特征关联方面存在局限性.
- 应用于光谱图的CNN通常无法明确捕捉光谱,时间和通道智能的特征相关性.
研究的目的:
- 为增强语音情感识别提出一种新的深层光谱时空通道网络 (DSTCNet).
- 提高表达式学习能力,以区分语言中的情绪.
- 解决传统CNN在捕捉特征关联方面的局限性.
主要方法:
- 开发了一个深度光谱时空通道网络 (DSTCNet),将光谱时空通道 (STC) 注意模块集成到CNN架构中.
- 提出了一个STC模块来推断跨时间,频率和通道维度的3D注意力图.
- 在柏林情感数据库 (EmoDB) 和IEMOCAP数据库上训练和评估DSTCNet.
主要成果:
- 与传统的基于CNN的方法相比,拟议的DSTCNet表现出优越的性能.
- 在语音情感识别方面,DSTCNet的表现优于现有的几种最先进的方法.
- STC注意力机制有效地将重点放在不同维度的关键语音特征上.
结论:
- 通过有效地建模光谱-时间通道特征关联,DSTCNet显著改善了语音情感识别.
- 拟议的STC注意力模块增强了深度学习模型从语音中学习歧视性情感表示的能力.
- DSTCNet为更具交互性和直观的人与计算机系统提供了一个有希望的进步.
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