Impact of different speaking modes on EMG-based speech recognition

We present our recent results on speech recognition by surface electromyography (EMG), which captures the electric potentials that are generated by the human articulatory muscles. This technique can be used to enable Silent Speech Interfaces, since EMG signals are generated even when people only articulate speech without producing any sound. Preliminary experiments have shown that the EMG signals created by audible and silent speech are quite distinct. In this paper we first compare various methods of initializing a silent speech EMG recognizer, showing that the performance of the recognizer substantially varies across different speakers. Based on this, we analyze EMG signals from audible and silent speech, present first results on how discrepancies between these speaking modes affect EMG recognizers, and suggest areas for future work.