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Syllable-based speech recognition using EMG.

Eduardo Lopez-Larraz1, Oscar M Mozos, Javier M Antelis

  • 1Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Spain. edulop@unizar.es

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
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This study introduces a silent-speech interface using facial electromyographic (EMG) signals for syllable recognition. The system achieves nearly 70% accuracy, offering a novel approach to silent communication technology.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Silent-speech interfaces aim to decode intended speech without audible vocalization.
  • Electromyographic (EMG) signals from facial muscles offer a non-invasive data source for such interfaces.
  • Current approaches often focus on phonemes or words, presenting challenges in segmentation and classification complexity.

Purpose of the Study:

  • To develop and evaluate a silent-speech interface utilizing electromyographic (EMG) signals.
  • To investigate the efficacy of syllable recognition as a middle ground between phoneme and word-level analysis.
  • To establish a robust method for transforming EMG signals into effective feature vectors for classification.

Main Methods:

  • Facial muscle electromyographic (EMG) signals were recorded.

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  • A novel approach based on syllable recognition was implemented.
  • EMG signals were converted into robust time-domain feature vectors.
  • A boosting classifier was trained using these feature vectors.

    • Main Results:

    • The system demonstrated effectiveness across three subjects.
    • A mean classification rate of approximately 70% was achieved for 30 distinct syllables.
    • The syllable-based approach proved advantageous for clear delimitation and reduced classification groups.

    Conclusions:

    • Syllable recognition is a viable and effective strategy for silent-speech interfaces based on facial EMG.
    • The developed system shows promise for advancing silent communication technologies.
    • Further research can build upon this approach for improved accuracy and a wider range of recognized units.