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在人类听觉通路中剖析神经计算,使用深度神经网络进行语音演讲

Yuanning Li^1,2, Gopala K Anumanchipalli^3,4, Abdelrahman Mohamed⁵

¹Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.

Nature neuroscience

|October 31, 2023

概括

深度神经网络 (DNN) 揭示了人类听觉系统如何处理语音. DNN模型显示了与从听觉神经到皮质的神经活动的显著对齐,推进了语音处理研究.

科学领域:

神经科学是一个神经科学.
计算式听觉神经科学计算式听觉神经科学
人工智能的人工智能

背景情况:

传统的线性模型在解释人类听觉系统复杂的语音处理方面取得了有限的成功.
人工神经网络 (ANN) 显示出模拟语音识别和处理的前景.
了解从听觉神经到语音皮层的神经编码对于破译语音感知至关重要.

研究的目的:

通过使用来自深度神经网络 (DNN) 模型的语音表示来研究人类听觉系统中的神经编码.
探索DNN层表示与整个听觉路径的神经活动之间的相关性.
为了确定无监督的语音模型是否可以有效地模拟听觉处理.

主要方法:

利用最先进的深度神经网络 (DNN) 模型与语音表示.
与听觉神经到语音皮层的神经活动数据相关联的等级DNN层表示.
对比无监督的DNN模型与监督和微调的语音处理任务模型.
研究了DNN模型对英语和普通话母语者皮质反应的预测.

主要成果:

DNN模型的层次层次显示出与整个上升的听觉系统中神经活动的强烈相关性.
无监督语音模型的表现与监督或微调模型相比.
更深层的DNN层与更高级的听觉皮层活动有更好的相关性,与语音和语法结构保持一致.
在英语或普通话中训练的DNN模型成功地预测了这些语言的母语使用者的皮质反应.

结论:

DNN模型表示与生物听觉通路融合,为理解语音处理提供了强大的框架.
这项研究提供了新的计算方法,用于模拟听觉皮层中的神经编码.
这些发现突出了ANN在阐明听觉感知中的复杂神经过程中的潜力.

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