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Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor.

Dafydd Ravenscroft1, Ioannis Prattis1, Tharun Kandukuri1

  • 1Department of Electrical Engineering, University of Cambridge, Cambridge CB3 0FA, UK.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a wearable graphene sensor for silent speech recognition, decoding throat muscle movements into intended speech. This technology offers a new way to communicate for individuals with voice impairments.

Keywords:
artificial neural networksgraphenemachine learningsilent speech recognitionstrain gauge

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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Machine Learning

Background:

  • Silent speech recognition aims to interpret intended speech without audible sound.
  • Applications include assisting individuals with voice impairments and secure communication.
  • Existing methods often lack wearability or accuracy.

Purpose of the Study:

  • To develop a wearable sensor for silent speech recognition.
  • To utilize graphene strain gauges for detecting throat muscle movements.
  • To apply machine learning for decoding these signals into intended speech.

Main Methods:

  • Fabrication of a flexible graphene strain gauge sensor on lycra fabric via screen printing.
  • Development of a machine learning framework to interpret sensor data.
  • Creation of a dataset with 15 words and 4 movements for training and testing.

Main Results:

  • Achieved 55% word accuracy and 85% movement accuracy in predicting intended speech.
  • Demonstrated the sensor's ability to detect subtle throat muscle movements and vibrations.
  • Validated the feasibility of the proposed silent speech recognition system.

Conclusions:

  • A wearable graphene strain gauge sensor combined with machine learning is a viable approach for silent speech recognition.
  • This technology holds promise for assistive communication devices.
  • Further research can enhance accuracy and expand the vocabulary recognition.