Decoding naturalistic speech from cortical surface recordings with sequence-to-sequence models

Summary

UCSF researchers decoded natural conversational speech from electrocorticography (ECoG) recordings using a sequence-to-sequence deep learning architecture. Decoding of 40 participants' ECoG during open-set speech production achieved median word error rates of 9.1%, approaching commercially viable performance for speech BCIs.