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Using rule-based knowledge to improve LVCSR

We show that an elaborate linguistic model of a natural language can be a valuable knowledge source to improve large vocabulary continuous speech recognition (LVCSR). Our approach is to complement a statistical language model with rule-based linguistic knowledge. A hidden Markov model based recognizer and an N-gram language model are used to compute a word lattice which is subsequently processed by a parser. We succeeded in enhancing recognition performance by favouring word sequences which the parser identified as being grammatically correct.