Finite-state transducer based hungarian LVCSR with explicit modeling of phonological changes

This article describes the design and the experimental evaluation of the first Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the recently proposed weighted finite state transducer (WFST) paradigm. The task domain is the recognition of fluently read sentences selected from a major daily newspaper. Recognition performance is evaluated using both monophone and triphone gender independent acoustic models. 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 in Hungarian. The language model is a statistical morpheme bigram model. Besides the basic list style pronunciation dictionary model we evaluate a novel phonology modeling component that describes the phonological changes prevalent in fluent Hungarian. Thanks to the flexible transducerbased architecture of the system the phonological component is integrated seamlessly with the basic modules with no need to modify the decoder itself. The proposed phonological model decreases the error rate by 8.32% relatively compared to the baseline triphone system. The morpheme error rate of the best configuration is 17.74% in a 1200 morpheme task with test set perplexity 70.