Towards excluding redundancy in electrode grid for automatic speech recognition based on surface EMG
In our previous studies, an electrode grid was effective for Japanese vowel recognition from surface electromyography, and it was illustrated that a feature selection method compressing the features to one twentieth of the total features could be achieved without severe decline in recognition accuracies with one subject. In this study, we further verify the validity of the method with six subjects and explore more appropriate electrode locations without redundancy that can be generalizable over subjects. The results of this study indicate that feature selection can be realized while discriminative powers are kept to some extent for the all subjects. In addition, the channels in the central part of the electrode gird can be regarded as redundant with respect to some subjects. Thus, combining the dense measurements provided by the electrode grid and the feature selection method is an effective approach to explore appropriate measurement location without redundancy for the sEMG-ASR.
redundancy electrode grid feature selection surface electromyography (semg) semg-based automatic speech recognition sparse discriminant analysis