Using Morphological Information for Robust Language Modeling in Czech ASR System

Abstract

Automatic speech recognition, or more precisely language modeling, of the Czech language has to face challenges that are not present in the language modeling of English. Those include mainly the rapid vocabulary growth and closely connected unreliable estimates of the language model parameters. These phenomena are caused mostly by the highly inflectional nature of the Czech language. On the other hand, the rich morphology together with the well-developed automatic systems for morphological tagging can be exploited to reinforce the language model probability estimates. This paper shows that using rich morphological tags within the concept of class-based n-gram language model with many-to-many word-to-class mapping and combination of this model with the standard word-based n-gram can improve the recognition accuracy over the word-based baseline on the task of automatic transcription of unconstrained spontaneous Czech interviews.