Open AccessRestoring speech following total removal of the larynx by a learned transformation from sensor data to acoustics
Author(s) -
James M. Gilbert,
José A. González,
Lam A. Cheah,
Stephen R. Ell,
Phil Green,
Roger K. Moore,
Ed Holdsworth
Publication year - 2017
Publication title -
the journal of the acoustical society of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 187
eISSN - 1520-8524
pISSN - 0001-4966
DOI - 10.1121/1.4978364
Subject(s) - acoustics , computer science , transformation (genetics) , speech recognition , signal (programming language) , larynx , identity (music) , field (mathematics) , physics , mathematics , medicine , biochemistry , surgery , pure mathematics , gene , programming language , chemistry
Total removal of the larynx may be required to treat laryn- geal cancer: speech is lost. This article shows that it may be possible to restore speech by sensing movement of the remaining speech articula- tors 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 tech- nique involves attaching small, unobtrusive magnets to the lips and tongue and monitoring changes in the magnetic field induced by their movement
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