Feature selection for vowel recognition based on surface electromyography derived with multichannel electrode grid

Abstract

This paper investigates how feature selection influences the accuracy of vowel recognition based on surface electromyography (sEMG) derived with an electrode grid, which consists of densely-spaced multielectrodes. In previous studies on sEMG-based automatic speech recognition (sEMG-ASR), disc electrodes or parallel bar electrodes were used and located empirically. But, in this study, to avoid missing out information about speech, an electrode grid was used to measure sEMG from the submental region during the production of five Japanese vowels. For feature selection, we applied sparse discriminant analysis (SDA) to the obtained data which can include some redundant signals. It was illustrated that feature selection compressing to one tenth or one twentieth of the total features could be achieved without steep decline in recognition accuracies. Combination of dense measurement based on the electrode grid and feature selection based on SDA is an effective approach for researches on sEMG-ASR. © 2012 Springer-Verlag.