Decoding speech perception from non-invasive brain recordings

Abstract Decoding speech from brain activity is a long-awaited goal in both healthcare and neuroscience. Invasive devices have recently led to major milestones in this regard: deep-learning algorithms trained on intracranial recordings can now start to decode elementary linguistic features such as letters, words and audio-spectrograms. However, extending this approach to natural speech and non-invasive brain recordings remains a major challenge. Here we introduce a model trained with contrastive learning to decode self-supervised representations of perceived speech from the non-invasive recordings of a large cohort of healthy individuals. To evaluate this approach, we curate and integrate four public datasets,…

• AN
  Agence Nationale de la Recherche

  Awards: ANR-17-EURE-, ANR-17-EURE-0017, 17-EURE-0017, ANR-17