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Related Experiment Videos

Robust and preceding speech detection using EMG.

H Manabe1, M Fukumoto

  • 1Multimedia Laboratories, NTT DoCoMo, Inc., Kanagawa, Japan.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
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This study introduces an electromyography (EMG)-based voice activity detection (VAD) system. This novel approach offers robust speech detection, outperforming traditional audio VAD in noisy environments and accurately identifying speech onset.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Speech Technology

Background:

  • Traditional voice activity detection (VAD) methods rely on audio signals, making them susceptible to acoustic noise and background interference.
  • Electromyography (EMG) signals offer an alternative modality for speech detection due to their inherent robustness against acoustic noise.
  • EMG signals have the potential to precede audible voice, enabling earlier speech event detection.

Purpose of the Study:

  • To propose and evaluate a novel EMG-based voice activity detection (VAD) system.
  • To demonstrate the effectiveness of EMG signals for robust and preceding speech detection.
  • To compare the performance of the proposed EMG-VAD against conventional audio-based VAD methods.

Main Methods:

  • Extraction of a single Voice Activity Detection (VAD) parameter from multiple EMG signal channels.

Related Experiment Videos

  • Experimental validation of the proposed EMG-based VAD method.
  • Comparison of EMG-VAD performance with traditional audio VAD in terms of speech head detection and noise insensitivity.

    • Main Results:

    • EMG signals were observed to reliably precede voice onset by at least 30 milliseconds.
    • The proposed EMG-based VAD method demonstrated a reduced rate of missing speech onsets compared to audio VAD.
    • The system exhibited relative insensitivity to background noise, maintaining performance in challenging acoustic conditions.

    Conclusions:

    • EMG-based VAD presents a viable and robust alternative to traditional audio-based methods.
    • The ability of EMG signals to precede voice allows for more timely speech event detection.
    • This technology holds promise for improved speech recognition and human-computer interaction systems, especially in noisy environments.