一个多扩展的卷积网络用于语音情绪识别和识别.
Samaneh Madanian1, Olayinka Adeleye2, John Michael Templeton3
1Department of Data Science and Artificial Intelligence, Auckland University of Technology, Auckland, New Zealand. sam.madanian@aut.ac.nz.
Scientific reports
|March 11, 2025
概括
这项研究引入了一种新的语音情感识别 (SER) 模型,使用光谱图的深度学习. 该模型增强了特征提取,并提高了基准数据集的准确性.
科学领域:
- 人工智能的人工智能
- 情感计算是一种情感计算.
- 语音处理 语音处理
背景情况:
- 语音情感识别 (SER) 对人机交互至关重要.
- 深度神经网络 (DNN) 和卷积神经网络 (CNN) 显示了使用语音谱图的 SER 的前景.
- 现有的方法在有效地从光谱图中学习深度模式方面面临挑战.
研究的目的:
- 提出一种新的SER模型,利用发言层次的谱图分析.
- 通过整合空间金字塔聚合 (SPP) 和注意力机制来增强特征提取.
- 适应面部识别技术,特别是ArcFace,以提高SER性能.
主要方法:
- 利用空间金字塔聚合 (SPP) 来克服CNN的大小限制.
- 使用SPP.提取了全球和多局部级别的特征向量.
- 实施了注意力模型来权衡提取的特征向量.
- 将通常用于人脸识别的ArcFace层应用于SER任务.
主要成果:
- 在IEMOCAP数据集上实现了67.9%的未加权精度.
- 在EMODB数据集上实现了77.6%的未加权精度.
- 通过拟议的模型架构,证明了 SER 性能的提高.
结论:
- 新的SER模型有效地从发言级谱图中学习深度模式.
- 整合SPP,注意力和ArcFace可以提高SER准确性.
- 拟议的方法为推进SER技术提供了一个有希望的方向.
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