On the use of morphological constraints in N-gram statistical language model

Abstract

State of the art Speech Recognition systems use statistical language modeling and in particular N-gram models to represent the language structure. The Arabic language has a rich morphology, which motivates the introduction of morphological constraints in the language model. Class-based N-gram models have shown satisfactory results, especially for language model adaptation and training from reduced datasets. They were also proven quite effective in their use of memory space. In this paper, we investigate a new morphological class-based language model. Morphological rules are used to derive the different words in a class from their stem. As morphological analyzer, a rule-based stemming method is proposed for the Arabic language. The language model has been evaluated on a database composed of articles from Lebanese newspaper Al-Nahar for the years 1998 and 1999. In addition, a linear interpolation between the N-gram model and the morphological model is also evaluated. Preliminary experiments detailed in this paper show satisfactory results.