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Our recent efforts towards developing a practical surface electromyography (sEMG) based silent speech recognition interface have resulted in significant advances in the hardware, software and algorithmic components of the system. In this paper, we report our algorithmic progress, specifically: sEMG feature extraction parameter optimization, advances in sEMG acoustic modeling, and sEMG sensor set reduction. The key findings are: 1) the gold-standard parameters for acoustic speech feature extraction are far from optimum for sEMG parameterization, 2) advances in state-of-the-art speech modelling can be leveraged to significantly enhance the continuous sEMG silent speech recognition accuracy, and 3) the number of sEMG sensors can be reduced by half with little impact on the final recognition accuracy, and the optimum sensor subset can be selected efficiently based on basic mono-phone HMM modeling.