EMG signals from orofacial muscles are mapped via linear transformation into self-supervised speech representation space to enable direct audio synthesis, shown on an ALS patient during silent articulation.
Development of semg sensors and algorithms for silent speech recognition.Journal of neural engineering, 15(4):046031, 2018
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Direct sequence-to-sequence EMG-to-text conversion for silent articulation using a geometric representation of high-dimensional signals, without audio targets or time-alignment.
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emg2speech: Synthesizing speech from electromyography using self-supervised speech models
EMG signals from orofacial muscles are mapped via linear transformation into self-supervised speech representation space to enable direct audio synthesis, shown on an ALS patient during silent articulation.
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Non-invasive electromyographic speech neuroprosthesis: a geometric perspective
Direct sequence-to-sequence EMG-to-text conversion for silent articulation using a geometric representation of high-dimensional signals, without audio targets or time-alignment.