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In this paper, we explore high performance software and hardware implementations of an automatic speech recognition system that can run locally on a mobile device. We automate the generation of key components of our speech recognition system using Three Fingered Jack, a tool for hardware/software codesign that maps computation to CPUs, data parallel processors, and custom hardware. We use Three Fingered Jack to explore energy and performance for two key kernels in our speech recognizer, the observation probability evaluation and across-word traversal. Through detailed hardware simulation and measurement, we produce accurate estimates for energy and area and show a significant energy improvement over a conventional mobile CPU.