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Updated: Jun 14, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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A Cross-Subject sEMG-to-Speech Conversion System Using Content Features and Model Calibration.

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 5, 2025
    PubMed
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    This study presents a novel surface electromyography-to-speech (ETS) system that reduces user burden by requiring less data and eliminating the need for synchronous speech recordings. The system achieves effective speech generation for individuals with speech impairments, even when trained with synthesized audio.

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    Area of Science:

    • Biomedical Engineering
    • Speech Technology
    • Machine Learning

    Background:

    • Current surface electromyography-to-speech (sEMG-ETS) systems demand extensive single-participant datasets and synchronous speech recordings for training.
    • These limitations hinder practical application, particularly for individuals with speech impairments.

    Purpose of the Study:

    • To develop a cross-subject sEMG-to-speech (ETS) conversion system that minimizes user data requirements and training burden.
    • To enable ETS technology for individuals facing language barriers by utilizing synthesized audio for model training.

    Main Methods:

    • Employed a pre-trained acoustic model to extract speaker-independent acoustic features.
    • Utilized Child Tune model calibration to adapt the acoustic model to new users' EMG features.
    • Investigated the use of electronically synthesized audio for training the ETS model as an alternative to human voice recordings.

    Main Results:

    • The proposed sEMG-to-speech (ETS) conversion system achieved a Character Error Rate (CER) of 21.71% after just 20 minutes of calibration data from new users.
    • Training the ETS model with synthesized audio yielded comparable CER results to training with human voice data.

    Conclusions:

    • The developed cross-subject ETS system significantly reduces the data burden for users.
    • The use of synthesized audio presents a viable and effective alternative for training ETS models, broadening accessibility for individuals with speech impairments.