Further Investigations on EMG-to-Speech Conversionby Matthias Janke, Michael Wand, Keigo Nakamura, Tanja Schultz
Abstract:
Our study deals with a Silent Speech Interface based onmapping surface electromyographic (EMG) signals to speech waveforms. Electromyographic signals recorded from the facial muscles capture the activity of the human articulatory apparatus and therefore allow to retrace speech, even when no audible signal is produced. The mapping of EMG signals to speech is done via a Gaussian mixture model (GMM)-based conversion technique. In this paper, we follow the lead of EMG-based speech-to-text systems and apply two major recent technological advances to our system, namely, we consider session- independent systems, which are robust against electrode repositioning, and we show that mapping the EMG signal to whispered speech creates a better speech signal than a mapping to normally spoken speech. We objectively evaluate the performance of our systems u sing a spectral distortion measure.
Reference:
Further Investigations on EMG-to-Speech Conversion (Matthias Janke, Michael Wand, Keigo Nakamura, Tanja Schultz), In IEEE International Conference on Acoustics, Speech and Signal Processing, 2012. (ICASSP)
Bibtex Entry:
@inproceedings{janke2012further,
year={2012},
title={Further Investigations on EMG-to-Speech Conversion},
note={ICASSP},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/Janke_FurtherInvestigationsEMG2F0.pdf},
abstract={Our study deals with a Silent Speech Interface based onmapping surface electromyographic (EMG) signals to speech waveforms. Electromyographic signals recorded from the facial muscles capture the activity of the human articulatory apparatus and therefore allow to retrace speech, even when no audible signal is produced. The mapping of EMG signals to speech is done via a Gaussian mixture model (GMM)-based conversion technique. In this paper, we follow the lead of EMG-based speech-to-text systems and apply two major recent technological advances to our system, namely, we consider session- independent systems, which are robust against electrode repositioning, and we show that mapping the EMG signal to whispered speech creates a better speech signal than a mapping to normally spoken speech. We objectively evaluate the performance of our systems u sing a spectral distortion measure.},
keywords={Silent Speech, Electromyography,Speech Synthesis, Voice Conversion},
author={Janke, Matthias and Wand, Michael and Nakamura, Keigo and Schultz, Tanja}
}