Human EEG and Recurrent Neural Networks Exhibit Common Temporal Dynamics During Speech Recognition.

Area of Science:

• Computational Neuroscience
• Artificial Intelligence
• Speech Processing

Background:

• Deep learning artificial neural networks excel at speech recognition, but the underlying mechanisms are not fully understood.
• Time-dependent features are crucial for speech perception in the human brain, studied via electroencephalography (EEG) and magnetoencephalography (MEG).
• Recurrent neural networks (RNNs) may emulate cortical dynamics through their temporal processing capabilities.

Purpose of the Study:

• To investigate commonalities in temporal dynamics between deep learning models and human brain activity during speech perception.
• To determine if RNNs exhibit envelope phase tracking, a phenomenon observed in human EEG.
• To compare representational similarity between RNNs and EEG signals for speech stimuli.

Main Methods:

• Presented identical sentences to human listeners and a Deep Speech RNN.
• Analyzed temporal dynamics of EEG signals and RNN units.
• Computed representational distance matrices (RDMs) for both brain and network responses.

Main Results:

• Both RNN hidden layers and human EEG signals demonstrated envelope phase tracking with comparable time lags.
• Representational distance matrices showed increasing similarity between RNN and EEG representations from early to later network layers.
• Similarity peaked at the recurrent layer of the Deep Speech RNN.

Conclusions:

• Deep Speech RNNs capture temporal speech features in a manner analogous to human brain processing.
• The findings suggest RNNs may partially emulate human cortical dynamics in speech perception.
• This research bridges artificial intelligence and neuroscience by highlighting shared temporal processing principles.