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.
Session-independent array-based emg-to- speech conversion using convolutional neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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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.