Mutual information analysis for feature and sensor subset selection in surface electromyography based speech recognition

In this paper, we investigate the use of surface electromyographic (sEMG) signals collected from articulatory muscles on the face and neck for performing automatic speech recognition. While previous work has typically used full-scale recognition experiments to evaluate appropriate feature representation schemes for sEMG signals, we present a systematic information-theoretic analysis for feature selection and optimal sensor subset selection. Our results indicate that Mel-cepstral frequency features are best suited for sEMG-based discrimination. Further, the sensor subset ranking obtained through the mutual information experiments are consistent with the results obtained from hidden Markov model based recognition. The framework presented here can be used for determining the best feature and sensor subset for a given speaker a priori, instead of determining them a posteriori from recognition experiments. We achieve a mean recognition accuracy of 80.6% with the best 5 sensor subset chosen by the MI analysis in comparison with 79.6% obtained from using the complete set of 11 sensors.