An adaptive-beam pruning technique for continuous speech recognition

Abstract

Pruning is an essential paradigm to build HMM based large vocabulary speech recognisers that use reasonable computing resources. Unlikely sentence, word or subword hypotheses are removed from the search space when their likelihood falls outside a beam relative to the best scoring hypothesis. A method for automatically steering this beam such that the search space attains a predefined size is presented.