Array-based Electromyographic Silent Speech Interfaceby Michael Wand, Christopher Schulte, Matthias Janke, Tanja Schultz
Abstract:
An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study is concerned with introducing an EMG recording system based on multi-channel electrode arrays. We first present our new system and introduce a method to deal with undertraining effects which emerge due to the high dimensionality of our EMG features. Second, we show that Independent Component Analysis improves the classification accuracy of the EMG array-based recognizer by up to 22.9% relative, which is a first example of an EMG signal processing method which is specifically enabled by our new array-based system. We evaluate our system on recordings of audible speech; achieving an optimal average word error rate of 10.9% with a training set of less than 10 minutes on a vocabulary of 108 words.
Reference:
Array-based Electromyographic Silent Speech Interface (Michael Wand, Christopher Schulte, Matthias Janke, Tanja Schultz), In 6th International Conference on Bio-inspired Systems and Signal Processing, 2013. (BIOSIGNALS 2013)
Bibtex Entry:
@inproceedings{wand2013array,
note={BIOSIGNALS 2013},
title={Array-based Electromyographic Silent Speech Interface},
year={2013},
booktitle={6th International Conference on Bio-inspired Systems and Signal Processing},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/BS13_WandSchulteJankeSchultz_ArrayBasedEMGSSI.pdf},
abstract={An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study is concerned with introducing an EMG recording system based on multi-channel electrode arrays. We first present our new system and introduce a method to deal with undertraining effects which emerge due to the high dimensionality of our EMG features. Second, we show that Independent Component Analysis improves the classification accuracy of the EMG array-based recognizer by up to 22.9% relative, which is a first example of an EMG signal processing method which is specifically enabled by our new array-based system. We evaluate our system on recordings of audible speech; achieving an optimal average word error rate of 10.9% with a training set of less than 10 minutes on a vocabulary of 108 words.},
author={Wand, Michael and Schulte, Christopher and Janke, Matthias and Schultz, Tanja}
}