Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

ABSTRACT: To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may overlook associated neural representations. By providing end-to-end accounts of speech-to-text transformation, new deep-learning systems could enable more complete brain models. We model EEG recordings o

SUBMITTER: Anderson AJ 

PROVIDER: S-EPMC11581396 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature