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Through out this work we explore different methods to integrate a complex Language Model (a hierarchical Language Model based on classes of phrases) into an Automatic Speech Recognition (ASR) system. The integration is carried out by means of a composition of the different Stochastic Finite State Automata associated to the specific Language Model. This method is based on the same idea employed to integrate the different knowledge sources involved in the recognition process when a classical word-based Language Model is considered. The obtained results show that this integrated architecture provides better ASR system performance than a two-pass decoder where the complex LM is employed to reorder the N-best list.

Using Finite State Models for the Integration of Hierarchical LMs into ASR Systems