Signal processing advances for the MUTE sEMG-based silent speech recognition system

Abstract

Military speech communication often needs to be conducted in very high noise environments. In addition, there are scenarios, such as special-ops missions, for which it is beneficial to have covert voice communications. To enable both capabilities, we have developed the MUTE (Mouthed-speech Understanding and Transcription Engine) system, which bypasses the limitations of traditional acoustic speech communication by measuring and interpreting muscle activity of the facial and neck musculature involved in silent speech production. This article details our recent progress on automatic surface electromyography (sEMG) speech activity detection, feature parameterization, multi-task sEMG corpus development, context dependent sub-word sEMG modeling, discriminative phoneme model training, and flexible vocabulary continuous sEMG silent speech recognition. Our current system achieved recognition accuracy at developable levels for a pre-defined special ops task. We further propose research directions in adaptive sEMG feature parameterization and data driven decision question generation for context-dependent sEMG phoneme modeling.