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Towards a Practical Silent Speech Interface Based on Vocal Tract Imaging

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TLDR
In this article, the authors describe advances in the development of an ultrasound silent speech interface for use in silent communications applications or as a speaking aid for persons who have undergone a laryngectomy.
Abstract
The paper describes advances in the development of an ultrasound silent speech interface for use in silent communications applications or as a speaking aid for persons who have undergone a laryngectomy. It reports some first steps towards making such a device lightweight, portable, interactive, and practical to use. Simple experimental tests of an interactive silent speech interface for everyday applications are described. Possible future improvements including extension to continuous speech and real time operation are discussed.

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Citations
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Journal ArticleDOI

Updating the Silent Speech Challenge benchmark with deep learning

TL;DR: In this paper, the authors present new results in which a 2010 benchmark study, called the Silent Speech Challenge, is updated with a Deep Learning strategy, using the same input features and decoding strategy as in the original Challenge article.
Journal ArticleDOI

Convolutional neural network-based automatic classification of midsagittal tongue gestural targets using B-mode ultrasound images

TL;DR: The CNN-based method achieves state-of-the-art performance, even though no pre-training of the CNN was carried out, and the speaker-dependent and speaker-independent tongue gestural target classification experiments are conducted.
Journal ArticleDOI

EchoWhisper: Exploring an Acoustic-based Silent Speech Interface for Smartphone Users

TL;DR: The EchoWhisper is proposed as a novel user-friendly, smartphone-based silent speech interface that takes advantage of the micro-Doppler effect of the acoustic wave resulting from mouth and tongue movements and assesses the acoustic features of beamformed reflected echoes captured by the dual microphones in the smartphone.
Proceedings ArticleDOI

DNN-based Acoustic-to-Articulatory Inversion using Ultrasound Tongue Imaging

TL;DR: This paper implemented several different Deep Neural Networks to estimate the articulatory information from the acoustic signal, and shows that CW-SSIM is the most useful error measure in the UTI context.
Journal ArticleDOI

Non-Invasive Silent Phoneme Recognition Using Microwave Signals

TL;DR: Electromagnetic transmission and reflection measurements of the vocal tract have great potential for future silent-speech interfaces, and are suggested to be a viable alternative to established methods.
References
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The HTK book

TL;DR: The Fundamentals of HTK: General Principles of HMMs, Recognition and Viterbi Decoding, and Continuous Speech Recognition.
Proceedings Article

Julius --- An Open Source Real-Time Large Vocabulary Recognition Engine

TL;DR: EUROSPEECH2001: the 7th European Conference on Speech Communication and Technology, September 3-7, 2001, Aalborg, Denmark.
Journal ArticleDOI

Silent speech interfaces

TL;DR: The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telecommunications privacy issues, and then follows with a presentation of demonstrator systems based on seven different types of technologies.
Journal ArticleDOI

Development of a silent speech interface driven by ultrasound and optical images of the tongue and lips

TL;DR: A segmental vocoder driven by ultrasound and optical images of the tongue and lips for a ''silent speech interface'' application, usable either by a laryngectomized patient or for silent communication.

Acquisition of Ultrasound, Video and Acoustic Speech Data for a Silent-Speech Interface Application

TL;DR: Synchronous acquisition of high-speed multimodal speech data, composed of ultrasound and optical images of the vocal tract together with the acoustic speech signal, for a silent speech interface is addressed.
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