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Found 5 papers, 1 papers with code
Decoding speech perception from non-invasive brain recordings
Overall, this effective decoding of perceived speech from non-invasive recordings delineates a promising path to decode language from brain activity, without putting patients at risk for brain surgery.
Toward a realistic model of speech processing in the brain with self-supervised learning
These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain.
Long-range and hierarchical language predictions in brains and algorithms
Predictive coding theory offers a potential explanation to this discrepancy: while deep language algorithms are optimized to predict adjacent words, the human brain would be tuned to make long-range and hierarchical predictions.
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e. g. regular speech versus scrambled words, sentences, or paragraphs).
Disentangling Syntax and Semantics in the Brain with Deep Networks
The activations of language transformers like GPT-2 have been shown to linearly map onto brain activity during speech comprehension.
