We present our research on continuous speech recognition based on Surface Electromyography (EMG), where speech information is captured by electrodes attached to the speaker's face. This method allows speech processing without requiring that an acoustic signal is present; however, reattachment of the EMG electrodes causes subtle changes in the recorded signal, which degrades the recognition accuracy and thus poses a major challenge for practical application of the system. Based on the growing body of recent work in domain-adversarial training of neural networks, we present a system which adapts the neural network frontend of our recognizer to data from a new recording session, without requiring supervised enrollment.
Cite as: Wand, M., Schultz, T., Schmidhuber, J. (2018) Domain-Adversarial Training for Session Independent EMG-based Speech Recognition. Proc. Interspeech 2018, 3167-3171, doi: 10.21437/Interspeech.2018-2318
@inproceedings{wand18_interspeech,
author={Michael Wand and Tanja Schultz and Jürgen Schmidhuber},
title={{Domain-Adversarial Training for Session Independent EMG-based Speech Recognition}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={3167--3171},
doi={10.21437/Interspeech.2018-2318}
}