Representation Learning of Tongue Dynamics for a Silent Speech Interface
Author:
Affiliation:
1. Zhejiang University of Water Resources and Electric Power
2. Institut Langevin, ESPCI Paris, PSL University, CNRS, Sorbonne Université
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E104.D/12/E104.D_2021EDP7090/_pdf
Reference15 articles.
1. [1] B. Denby, T. Schultz, K. Honda, T. Hueber, J.M. Gilbert, and J.S. Brumberg, “Silent speech interfaces,” Speech Communication, vol.52, no.4, pp.270-287, 2010. 10.1016/j.specom.2009.08.002
2. [2] Y. Ji, L. Liu, H. Wang, Z. Liu, Z. Niu, and B. Denby, “Updating the silent speech challenge benchmark with deep learning,” Speech Communication, vol.98, pp.42-50, 2018. 10.1016/j.specom.2018.02.002
3. [3] B. Denby and M. Stone, “Speech synthesis from ultrasound images of the tongue,” Proc. ICASSP, Montréal, Canada, 2004.
4. [4] E. Tatulli and T. Hueber, “Feature extraction using multimodal convolutional neural networks for visual speech recognition,” Proc. ICASSP, New Orleans, USA, March 2017. 10.1109/icassp.2017.7952701
5. [5] K. Xu, P. Roussel, T.G. Csapó, and B. Denby, “Convolutional neural network-based automatic classification of midsagittal tongue gestural targets using B-mode ultrasound images,” The Journal of the Acoustical Society of America, vol.141, no.6, EL531, 2017. 10.1121/1.4984122
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