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In this paper, we present a novel approach to integrate speech recognition and rulebased machine translation by lattice parsing. The presented approach is hybrid in two senses. First, it combines structural and statistical methods for language modeling task. Second, it employs a chart parser which utilizes manually created syntax rules in addition to scores(More)
In this paper, we present a powerful Arabic morphological analyzer and generator. The approach employs finite state machines enriched with unification capability. The presented system is used as a component in both statistical and rule based machine translation systems. We give detailed illustrations on how we handle nominal and verbal morphology in Arabic.(More)
In this paper, the recognition performances of several methodologies proposed in the context of Turkish Large Vocabulary Continuous Speech Recognition are retrieved by using a limited audio corpus. Word based, stem based, stem-ending based, and morph based language models are utilized with different n-gram orders. Word based and stem-ending based language(More)
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