ACG-EmoCluster:通过DeepCluster增强的情感语言捕获空间和时间信息的新型框架
Huan Zhao1, Lixuan Li1, Xupeng Zha1
1College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
我们开发了ACG-EmoCluster,这是一种用于语音情感识别 (SER) 的新型半监督框架. 它有效地提取空间和时间语音特征,在监督和半监督任务中表现优于现有的方法.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 语音情感识别 (SER) 面临着从语音数据中有效学习的挑战,因为其信息和性和时间连贯性很高.
- 为图像或文本设计的现有特征提取器对于语音数据来说并不理想.
- 开发强大的SER系统需要专门的方法来捕捉空间和时间语言特征.
研究的目的:
- 提出一个新的半监督框架,ACG-EmoCluster,用于增强语音情感识别.
- 开发一种功能提取器,能够同时从语音中捕获空间和时间特征.
- 通过通过集群分类器通过无监督学习来改善语音表示.
主要方法:
- ACG-EmoCluster框架将一个用于空间特征提取的Attn-Convolution神经网络与一个用于时间特征提取的双向门式循环单元 (BiGRU) 结合起来.
- Attn-Convolution网络提供了一个全球空间接收场,可适应各种数据尺度.
- BiGRU用于学习时间信息,在较小的数据集上特别有效,以减少数据依赖.
主要成果:
- 对MSP-Podcast数据集的实验结果表明,该框架能够捕获有效的语音表示.
- 在监督和半监督的SER任务中,ACG-EmoCluster显著优于所有基线方法.
- 拟议的方法在从语音中提取有意义的特征以识别情绪方面表现出卓越的表现.
结论:
- ACG-EmoCluster框架为半监督的语音情感识别提供了一个强大的方法.
- 同时提取空间和时间特征对于有效的SER至关重要.
- 拟议的方法减轻了数据依赖,并增强了语音表示学习.
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