Using syntactic information to improve large-vocabulary word recognition

A global, context-sensitive parsing procedure that aids a large-vocabulary isolated-word speech recognition system is described. Presented with a long sequence of word candidates, the parser identifies likely erroneous words on the basis of a syntactic analysis of the preceding words. The parser suggests likely locations in the word sequence for punctuation marks such as sentence-final periods. It is not as powerful as semantic trigram language models, but it requires much less memory and training. One significant advantage is that it exploits sentence structure well beyond the three-word limit of trigram models.<
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