A Spectral Mapping Method for EMG-based Recognition of Silent Speech

This paper reports on our latest study on speech recognition based on surface electromyography (EMG). This technology allows for Silent Speech Interfaces since EMG captures the electrical potentials of the human articulatory muscles rather than the acoustic speech signal. Therefore, our technology enables speech recognition to be applied to silently mouthed speech. Earlier experiments indicate that the EMG signal is greatly impacted by the mode of speaking. In this study we analyze and compare EMG signals from audible, whispered, and silent speech. We quantify the differences and develop a spectral mapping method to compensate for these differences. Finally, we apply the spectral mapping to the front-end of our speech recognition system and show that recognition rates on silent speech improve by up to 12.3% relative.