Computer speech recognition has been very successful in limited domains and for isolated word recognition. However, widespread use of large-vocabulary continuous- speech recognizers is limited by the speed of current recognizers, which cannot reach acceptable error rates while running in real time. This paper shows how to harness shared memory multiprocessors, which are becoming increasingly common, to increase significantly the speed, and therefore the accuracy or vocabulary size, of a speech recognizer. We describe the parallelization of an existing high-quality speech recognizer, achieving a speedup of a factor of 3, 5 and 6 on 4, 8 and 12 processors respectively for the benchmark North American business news (NAB) recognition task.