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Likelihood evaluation can substantially affect the total computational load for continuous hidden Markov model (HMM)-based speech-recognition systems with small vocabularies. This letter presents feature pruning , a simple yet effective technique to reduce computation and, hence, power consumption of likelihood evaluation. Our technique, under certain conditions, only evaluates the likelihoods of a fraction of feature elements and approximates those of the remaining (pruned) ones by a simple function. The order in which feature elements are evaluated is obtained by a data-driven approach to minimize computation. With this order, feature pruning can speed up the likelihood evaluation by a factor of 1.3-1.8 and reduce its power consumption by 27%-43% for various recognition tasks, including those in noisy environments.