This article introduces a novel approach to model phonology and morphosyntax in morpheme unit based speech recognizers. The proposed method is evaluated in our recent Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the weighted finite state transducer (WFST) paradigm. The task domain is the recognition of fluently read sentences selected from a major daily newspaper. The vocabulary units used in the system are morpheme based in order to provide sufficient coverage of the large number of word-forms resulting from affixation and compounding. Besides the basic pronunciation model and the morpheme N-gram language model we evaluate a novel phonology model and the novel stochastic morphosyntactic language model (SMLM). Thanks to the flexible transducer-based architecture of the system these new components are integrated seamlessly with the basic modules with no need to modify the decoder itself. The proposed phonology model reduced the error rate by 8.32% and the stochastic morphosyntactic language model decreased the error rate by 17.9% relatively compared to the baseline systems. The morpheme error rate of the best configuration is 14.75% in a 1350 morpheme Hungarian dictation task.
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