Sensor subset selection for surface electromyograpy based speech recognition

The authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (sEMG) sensors in fixed recording locations on the face and neck. The original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech production. In this paper we systematically analyzed speech recognition performance from sensor subsets with the goal of reducing the number of sensors needed and finding the best combination of sensor locations to achieve word recognition rates comparable to the full set. We evaluated each of the different possible subsets by its mean word recognition rate across nine speakers using HMM modeling of MFCC and co-activation features derived from the subset of sensor signals. We show empirically that five sensors are sufficient to achieve a recognition rate to within a half a percentage point of that obtainable from the full set of sensors.