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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Signal acquisition and processing techniques for sEMG based silent speech recognition.

Geoffrey S Meltzner1, Glen Colby, Yunbin Deng

  • 1BAE Systems, Inc, Burlington, MA 01809, USA. geoffrey.meltzner@baesystems.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

Surface electromyography (sEMG) silent speech recognition uses muscle signals to interpret speech, overcoming acoustic limitations. This study details signal acquisition and processing methods to address unique challenges in developing this technology.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Acoustic speech recognition faces limitations in noisy environments or for individuals with speech impairments.
  • Surface electromyography (sEMG) offers an alternative by capturing muscle activity during speech production.
  • Developing robust sEMG-based silent speech recognition requires addressing signal acquisition and processing challenges.

Purpose of the Study:

  • To describe signal acquisition strategies for sEMG-based silent speech recognition.
  • To outline signal processing techniques developed to overcome challenges in sEMG-based silent speech recognition.
  • To present strategies employed during the development of a silent speech recognition system using sEMG.

Main Methods:

  • Utilized surface electromyography (sEMG) to measure facial and neck muscle activity associated with speech.
  • Developed specific signal acquisition protocols to capture high-quality sEMG data.
  • Implemented advanced signal processing algorithms for noise reduction and feature extraction from sEMG signals.

Main Results:

  • Successfully acquired interpretable sEMG data from speech-related musculature.
  • Demonstrated effective noise reduction and artifact removal in sEMG signals.
  • Developed a processing pipeline amenable to real-time silent speech recognition.

Conclusions:

  • sEMG-based silent speech recognition is a viable alternative to acoustic methods.
  • Careful signal acquisition and sophisticated processing are crucial for system performance.
  • The presented strategies provide a foundation for advancing silent speech recognition technology.