Restoring speech following total removal of the larynx by a learned transformation from sensor data to acoustics-Reference-Cited by-同舟云学术

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

Published:2017-03 Issue:3 Volume:141 Page:EL307-EL313
ISSN:0001-4966
Container-title:The Journal of the Acoustical Society of America
language:en
Short-container-title:The Journal of the Acoustical Society of America

Author:

Gilbert James M.¹,Gonzalez Jose A.²,Cheah Lam A.¹,Ell Stephen R.³,Green Phil⁴,Moore Roger K.⁴,Holdsworth Ed⁵

Affiliation:

1. School of Engineering, University of Hull, Kingston upon Hull, United Kingdom

2. Department of Computer Science, University of Sheffield, Sheffield, United Kingdom

3. Hull and East Yorkshire Hospitals Trust, Castle Hill Hospital, Cottingham, United Kingdom

4. Department of Computer Science, University of Sheffield, Sheffield, United Kingdom,

5. Practical Control Limited, Sheffield, United Kingdom

Publisher

Acoustical Society of America (ASA)

Subject

Acoustics and Ultrasonics,Arts and Humanities (miscellaneous)

Link

http://asa.scitation.org/doi/pdf/10.1121/1.4978364

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1. DAFT-Net: Dual Attention and Fast Tongue Contour Extraction Using Enhanced U-Net Architecture;Entropy;2024-05-31

2. Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient;Current Opinion in Otolaryngology & Head & Neck Surgery;2023-11-28

3. Evaluation of different antenna types and positions in a stepped frequency continuous-wave radar-based silent speech interface;Interspeech 2022;2022-09-18

4. Silent Speech Interfaces for Speech Restoration: A Review;IEEE Access;2020

5. Ultra2Speech - A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images;Medical Image Computing and Computer Assisted Intervention – MICCAI 2020;2020

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