Speech synthesis from neural decoding of spoken sentences

Publication/Creation Date
April 24 2019
Edward Chang (creator)
Gopala Anumanchipalli (creator)
Josh Chartier (creator)
University Of California, San Francisco (creator)
Media Type
Journal Article
Persuasive Intent

Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract articulators. Here we designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. Intermediate articulatory dynamics enhanced performance even with limited data. Decoded articulatory representations were highly conserved across speakers, enabling a component of the decoder to be transferrable across participants. Furthermore, the decoder could synthesize speech when a participant silently mimed sentences. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication.
HCI Platform
Discursive Type
Location on Body
Marketing Keywords

Date archived
April 25 2019
Last edited
April 25 2019
How to cite this entry
Edward Chang, Gopala Anumanchipalli, Josh Chartier, University of California, San Francisco. (April 24 2019). "Speech synthesis from neural decoding of spoken sentences". Nature: International Journal of Science. Springer Nature Publishing. Fabric of Digital Life. https://fabricofdigitallife.com/index.php/Detail/objects/3814