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Restoring speech following total removal of the larynx by a learned transformation from sensor data to acoustics

Gilbert, James M.; Gonzalez, Jose A.; Cheah, Lam A.; Ell, Stephen R.; Green, Phil; Moore, Roger K.; Holdsworth, Ed


Jose A. Gonzalez

Lam A. Cheah

Stephen R. Ell

Phil Green

Roger K. Moore

Ed Holdsworth


Total removal of the larynx may be required to treat laryngeal cancer: speech is lost. This article shows that it may be possible to restore speech by sensing movement of the remaining speech articulators and use machine learning algorithms to derive a transformation to convert this sensor data into an acoustic signal. The resulting “silent speech,” which may be delivered in real time, is intelligible and sounds natural. The identity of the speaker is recognisable. The sensing technique involves attaching small, unobtrusive magnets to the lips and tongue and monitoring changes in the magnetic field induced by their movement.


Gilbert, J. M., Gonzalez, J. A., Cheah, L. A., Ell, S. R., Green, P., Moore, R. K., & Holdsworth, E. (2017). Restoring speech following total removal of the larynx by a learned transformation from sensor data to acoustics. The Journal of the Acoustical Society of America, 141(3), EL307-EL313.

Journal Article Type Article
Acceptance Date Feb 21, 2017
Online Publication Date Mar 21, 2017
Publication Date 2017-03
Deposit Date Mar 28, 2017
Publicly Available Date Dec 5, 2018
Journal Journal of the Acoustical Society of America
Print ISSN 0001-4966
Electronic ISSN 1520-8524
Publisher Acoustical Society of America
Peer Reviewed Peer Reviewed
Volume 141
Issue 3
Pages EL307-EL313
Keywords Speech restoration; Permanent magnetic articulography; Machine learning
Public URL
Publisher URL
Related Public URLs
Additional Information received: 2016-10-17; revised: 2017-01-22; accepted: 2017-02-21; published: 2017-03-21


Article (603 Kb)

Copyright Statement
© 2017 Acoustical Society of America

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