Multiple Text Segmentation for Statistical Language Modeling

International audienceIn this article we deal with the text segmentation problem in statistical language modeling for under-resourced languages with a writing system without word boundary delimiters. While the lack of text resources has a negative impact on the performance of language models, the errors introduced by the automatic word segmentation makes those data even less usable. To better exploit the text resources, we propose a method based on weighted finite state transducers to estimate the N-gram language model from the training corpus on which each sentence is segmented in multiple ways instead of a unique seg-mentation. The multiple segmentation generates more N-grams from the training corpus and allows obtaining the N-grams not found in unique segmentation. We use this approach to train the language models for automatic speech recognition systems of Khmer and Vietnamese languages and the multiple segmenta-tions lead to a better performance than the unique segmentation approach