An adaptive-beam pruning technique for continuous speech recognition
نویسندگان
چکیده
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 prede ned size is presented.
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