On the similarities of representations in artificial and brain neural networks for speech recognition.

ABSTRACT:

Introduction

In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system.

Methods

Utilizing high-performance automatic speech recognition systems, and advanced non-invasive human neuroimaging technology such as magnetoencephalography and multivariate pattern-information analysis, the current study aimed to relate machine-learned representations of speech to recorded human brain representations of the same speech.

Results

In one

SUBMITTER: Wingfield C 

PROVIDER: S-EPMC9811675 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature