Embedding grammars into statistical language models

This work combines grammars and statistical language models for speech recognition together in the same sentence. The grammars are compiled into bigrams with word indices, which serve to distinguish different syntactic positions of the same word. For both the grammatical and statistical parts there is one common interface for obtaining a language model score for bior trigrams. With only a small modification to a recogniser prepared for statistical language models, this new model can be applied without using a parser or a finite-state network in the recogniser. Priority is given to the grammar, therefore the combined model is able to disallow certain word transitions. With this combined language model, one or several grammatical phrases can be embedded into longer sentences.