使用卷积神经网络对语言语音表示进行强大的神经跟踪
Corentin Puffay1,2, Jonas Vanthornhout1, Marlies Gillis1
1Department Neurosciences, ExpORL, KU Leuven, Leuven, Belgium.
Journal of neural engineering
|August 18, 2023
概括
一个新的非线性卷积神经网络 (CNN) 模型有效地从脑活动 (EEG) 中解码语言信息. 这种先进的模型显著改进了理解大脑语言处理的线性方法.
科学领域:
- 听觉神经科学 听觉神经科学
- 计算语言学计算语言学
- 大脑与计算机的接口.
背景情况:
- 大脑中的神经活动在听觉感知过程中跟踪各种语音信号特征.
- 电脑电图 (EEG) 结合语音信号可以测量神经跟踪.
- 线性模型显示语言特征有助于神经跟踪,但无法捕捉非线性大脑动态.
研究的目的:
- 开发和评估一种非线性卷积神经网络 (CNN) 模型,用于分析语音语言特征的神经跟踪.
- 为了研究特定的语言特征 (语音惊喜,队列,单词惊喜,单词频率) 在词汇信息之外的贡献.
- 为了比较非线性CNN与线性编码器和线性化的CNN模型的性能.
主要方法:
- 在非线性CNN框架内整合语音和基于单词的语言特征.
- 使用语音和词语发作作为控制来隔离语言信息.
- 对非线性CNN与线性编码器和线性化的CNN模型进行比较分析.
主要成果:
- 非线性CNN证明了队列 (CE) 对语音发病的显著贡献.
- 单词惊喜 (WS) 和单词频率 (WF) 显示出对单词发病的显著贡献.
- 非线性CNN模型的表现始终优于线性基线模型.
结论:
- 非线性建模,特别是CNN,为测量语音中语言信息的神经编码提供了更敏感的方法.
- 这种非线性方法揭示了线性模型可能无法检测到的效应,可能改善客观语音理解措施.
- 这些发现表明,在未来的听觉神经科学研究中,有可能加强主体内语音感知分析,减少录制时间.
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