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Speech synthesis from neural decoding of spoken sentences

Author

Listed:
  • Gopala K. Anumanchipalli

    (University of California San Francisco
    University of California San Francisco)

  • Josh Chartier

    (University of California San Francisco
    University of California San Francisco
    University of California Berkeley and University of California San Francisco Joint Program in Bioengineering)

  • Edward F. Chang

    (University of California San Francisco
    University of California San Francisco
    University of California Berkeley and University of California San Francisco Joint Program in Bioengineering)

Abstract

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.

Suggested Citation

  • Gopala K. Anumanchipalli & Josh Chartier & Edward F. Chang, 2019. "Speech synthesis from neural decoding of spoken sentences," Nature, Nature, vol. 568(7753), pages 493-498, April.
  • Handle: RePEc:nat:nature:v:568:y:2019:i:7753:d:10.1038_s41586-019-1119-1
    DOI: 10.1038/s41586-019-1119-1
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    Cited by:

    1. Joshua Kosnoff & Kai Yu & Chang Liu & Bin He, 2024. "Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. You Wang & Ming Zhang & Ruifen Hu & Guang Li & Nan Li, 2020. "Silent Speech Recognition for BCI - A Review," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 27(2), pages 20625-20627, April.
    3. Taemin Kim & Yejee Shin & Kyowon Kang & Kiho Kim & Gwanho Kim & Yunsu Byeon & Hwayeon Kim & Yuyan Gao & Jeong Ryong Lee & Geonhui Son & Taeseong Kim & Yohan Jun & Jihyun Kim & Jinyoung Lee & Seyun Um , 2022. "Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Suseendrakumar Duraivel & Shervin Rahimpour & Chia-Han Chiang & Michael Trumpis & Charles Wang & Katrina Barth & Stephen C. Harward & Shivanand P. Lad & Allan H. Friedman & Derek G. Southwell & Saurab, 2023. "High-resolution neural recordings improve the accuracy of speech decoding," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    5. Sean L. Metzger & Jessie R. Liu & David A. Moses & Maximilian E. Dougherty & Margaret P. Seaton & Kaylo T. Littlejohn & Josh Chartier & Gopala K. Anumanchipalli & Adelyn Tu-Chan & Karunesh Ganguly & E, 2022. "Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. Junfeng Lu & Yuanning Li & Zehao Zhao & Yan Liu & Yanming Zhu & Ying Mao & Jinsong Wu & Edward F. Chang, 2023. "Neural control of lexical tone production in human laryngeal motor cortex," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Xiao-yu Sun & Bin Ye, 2023. "The functional differentiation of brain–computer interfaces (BCIs) and its ethical implications," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    8. Prateek Jain & Alberto Garcia Garcia, 2022. "Quantum classical hybrid neural networks for continuous variable prediction," Papers 2212.04209, arXiv.org, revised Mar 2023.
    9. Sarah K. Wandelt & David A. Bjånes & Kelsie Pejsa & Brian Lee & Charles Liu & Richard A. Andersen, 2024. "Representation of internal speech by single neurons in human supramarginal gyrus," Nature Human Behaviour, Nature, vol. 8(6), pages 1136-1149, June.

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