循环神经网络作为人类语音识别的神经计算模型
Christian Brodbeck1,2, Thomas Hannagan2,3, James S Magnuson2,4,5
1Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada.
PLoS computational biology
|July 28, 2025
概括
循环神经网络 (RNN),特别是长期短期记忆 (LSTM) 模型,可以在计算上模仿人类语音识别. 它们的内部动力学预测神经反应,这表明RNN是皮层语音处理的可行模型.
科学领域:
- 计算神经科学是一种神经科学.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 人类语音识别包括将连续的声信号处理成离散的语言单元.
- 循环神经网络 (RNN) 处理顺序数据,使它们成为语言时间信息处理的潜在模型.
- 以前的研究表明,RNN可以模拟语音和语言处理的行为方面,但它们的神经动力学仍然未经检查.
研究的目的:
- 调查RNN的内部计算动态,当训练用于语音识别时,是否类似于人类神经语音处理.
- 确定RNN是否可以预测人类神经群体对听觉刺激的反应.
- 探索RNN架构中的认知原则如何影响它们对人类神经活动的预测能力.
主要方法:
- 训练有素的长期短期记忆 (LSTM) RNN 在听觉谱图上进行语音识别任务.
- 将RNN内部动态的预测精度与人类神经反应的听觉特征进行了比较.
- 修改了基于认知原则的RNN架构,例如语音竞争,以评估对时间动态的影响.
主要成果:
- 训练用于语音识别的LSTM RNNs的内部动态显著预测了人类神经群体的反应.
- RNN预测超过了仅基于听觉特征的预测,这表明了更深层次的处理.
- 受人类语音竞争启发的建筑修改增强了RNN模拟人类类型时间动态的能力.
结论:
- RNN,特别是LSTM,为人类语音识别背后的皮质机制提供了可信的计算模型.
- 这项研究表明,RNN内部动态可以在语音感知过程中反映人类大脑中的神经过程.
- 认知原则可以集成到RNNs中,以提高它们作为人类神经计算模型的忠实性.
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