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Shift invariant feature extraction for sEMG-based speech recognition with electrode grid.

Takatomi Kubo, Masaki Yoshida, Takumu Hattori

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances Japanese vowel recognition using surface electromyography (sEMG) by applying a spatial shift invariant feature extraction method. This technique improves accuracy by compensating for electrode placement variations.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Speech Recognition

    Background:

    • Surface electromyography (sEMG) electrode grids are effective for Japanese vowel recognition.
    • Variations in electrode grid attachment sites can affect recognition accuracy.

    Purpose of the Study:

    • To further leverage electrode grids for improved Japanese vowel recognition.
    • To introduce and verify a spatial shift invariant feature extraction method to compensate for electrode placement deviations.

    Main Methods:

    • Utilized a 2-D dual tree complex wavelet transform as the spatial shift invariant feature extraction method.
    • Applied the method to sEMG data for Japanese vowel recognition.

    Main Results:

    • The spatial shift invariant feature extraction method significantly improved recognition accuracy.
    • This method provides additional, valuable information compared to independent channel signal analysis.

    Conclusions:

    • Spatial shift invariant feature extraction is efficient for enhancing sEMG-based Japanese vowel recognition.
    • Compensating for electrode placement variability is crucial for robust speech recognition systems.