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Speech neuroprostheses have the potential to restore communication to people living with paralysis, but naturalistic speed and expressivity are elusive1. Here we use high-density surface recordings of the speech cortex in a clinical-trial participant with severe limb and vocal paralysis to achieve high-performance real-time decoding across three complementary speech-related output modalities: text, speech audio and facial-avatar animation. We trained and evaluated deep-learning models using neural data collected as the participant attempted to silently speak sentences. For text, we demonstrate accurate and rapid large-vocabulary decoding with a median rate of 78 words per minute and median word error rate of 25%. For speech audio, we demonstrate intelligible and rapid speech synthesis and personalization to the participant’s pre-injury voice. For facial-avatar animation, we demonstrate the control of virtual orofacial movements for speech and non-speech communic

United statesPaulh brookesJd organizationInternational conference on learning representationsPacific rim conference on communications computersInternational conference on machineVirtual user forumCurran associatesAssociation for computational linguisticsInternational conference on machine learning volInternational conference on neural information processing systemsWorkshop at international conference on learning representationsInternational conference on acousticsInternational brainInformation processing association annual summitBrain computer interface