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Discriminative feature modeling for statistical speech recognition ...
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| 82 |
Towards Learning Terminological Concept Systems from Multilingual Natural Language Text ...
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| 83 |
How the input shapes the acquisition of verb morphology: elicited production and computational modelling in two highly inflected languages ...
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| 84 |
Which Theory of Language for Deep Neural Networks? Speech and Cognition in Humans and Machines ...
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Capone, Luca. - : Technology and Language, 2(4), 29-60, 2021
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| 85 |
Data-Driven Analysis of Zebra Finch Song Copying and Learning
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| 86 |
Sentiment Analysis of Amazon Electronic Product Reviews using Deep Learning
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| 87 |
Phonetic processing in speech sound disorder (Gerwin et al., 2021) ...
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| 88 |
Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor. ...
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| 89 |
Phonetic processing in speech sound disorder (Gerwin et al., 2021) ...
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| 90 |
Automated Paraphrase Quality Assessment Using Language Models and Transfer Learning
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In: Computers; Volume 10; Issue 12; Pages: 166 (2021)
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| 91 |
Extracting Semantic Relationships in Greek Literary Texts
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In: Sustainability ; Volume 13 ; Issue 16 (2021)
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| 92 |
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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| 93 |
Burnout, Resilience, and COVID-19 among Teachers: Predictive Capacity of an Artificial Neural Network
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In: Applied Sciences ; Volume 11 ; Issue 17 (2021)
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| 94 |
Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
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In: Journal of Risk and Financial Management ; Volume 14 ; Issue 11 (2021)
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| 95 |
Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection
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In: Future Internet; Volume 14; Issue 1; Pages: 4 (2021)
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| 96 |
Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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| 97 |
Optimization of Convolutional Neural Networks Architectures Using PSO for Sign Language Recognition
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In: Axioms; Volume 10; Issue 3; Pages: 139 (2021)
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| 98 |
Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments
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In: Sensors ; Volume 21 ; Issue 22 (2021)
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| 99 |
Sentence Compression Using BERT and Graph Convolutional Networks
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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| 100 |
Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
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In: Future Internet ; Volume 13 ; Issue 11 (2021)
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