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Ultrasonic Doppler Based Silent Speech Interface Using Perceptual Distance
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In: Applied Sciences; Volume 12; Issue 2; Pages: 827 (2022)
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Exploring Silent Speech Interfaces Based on Frequency-Modulated Continuous-Wave Radar
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In: Sensors; Volume 22; Issue 2; Pages: 649 (2022)
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Haptic performativity: exploring the force of bodies and the limits of linguistic action in silent protests
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Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor. ...
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Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification
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In: Sensors ; Volume 21 ; Issue 20 (2021)
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Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor
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In: Sensors; Volume 22; Issue 1; Pages: 299 (2021)
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Abstract:
Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.
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Keyword:
artificial neural networks; graphene; machine learning; silent speech recognition; strain gauge
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URL: https://doi.org/10.3390/s22010299
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Women in the New Testament’s Church Ministry: The Problem of Remaining Silent
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In: Diligence: Journal of the Liberty University Online Religion Capstone in Research and Scholarship (2018)
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Variation de la durée des pauses silencieuses : impact de la syntaxe, du style de parole et des disfluences
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In: Langages, N 211, 3, 2018-08-29, pp.13-40 (2018)
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Neural and Behavioral Mechanisms of Clear Speech
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In: Graduate Theses and Dissertations (2017)
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Невербальные средства общения в диалогическом дискурсе терциарной речи
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БАБАЯН ВЛАДИМИР НИКОЛАЕВИЧ. - : Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования «Вятский государственный гуманитарный университет», 2016
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Articulation in multimodal silent speech interface for European Portuguese ; Interfaces de fala silenciosa multimodais para português europeu com base na articulação
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Charting the recovery of dysphagia in two complex cases of post-thermal burn injury: physiological characteristics and functional outcomes
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Preliminary Test of a Real-Time, Interactive Silent Speech Interface Based on Electromagnetic Articulograph
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In: CSE Conference and Workshop Papers (2014)
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Individual articulator's contribution to phoneme production
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In: CSE Conference and Workshop Papers (2013)
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Word Recognition from Continuous Articulatory Movement Time-Series Data using Symbolic Representations
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In: CSE Conference and Workshop Papers (2013)
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