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This paper describes a robust, accurate, efficient, low-resource, medium-vocabulary, grammar-based speech recognition system using Hidden Markov Models for mobile applications. Among the issues and techniques we explore are improving robustness and efficiency of the front-end, using multiple microphones for removing extraneous signals from speech via a new multi-channel CDCN technique, reducing computation via silence detection, applying the Bayesian information criterion (bic) to build smaller and better acoustic models, minimizing finite state grammars, using hybrid maximum likelihood and discriminative models, and automatically generating baseforms from single new-word utterances.