Total: 1
This paper deals with a Silent Speech Interface based on Surface Electromyography (EMG), where electrodes capture the electric activity generated by the articulatory muscles from a user's face in order to decode the underlying speech, allowing speech to be recognized even when no sound is heard or created. So far, most EMG-based speech recognizers described in literature do not allow electrode reattachment between system training and usage, which we consider unsuitable for practical applications. In this study we report on our research on unsupervised session adaptation: A system is pre-trained with data from multiple recording sessions and then adapted towards the current recording session using data accruable during normal use, without requiring a time-consuming specific enrollment phase. We show that considerable accuracy improvements can be achieved with this method, paving the way towards real-life applications of the technology.