A Toolkit for Efficient Learning of Lexical Units for Speech Recognition

String segmentation is an important and recurring problem in natural language processing and other domains. For morphologically rich languages, the amount of different word forms caused by morphological processes like agglutination, compounding and inflection, may be huge and causes problems for traditional word-based language modeling approach. Segmenting text into better modelable units is thus an important part of the modeling task. This work presents methods and a toolkit for learning segmentation models from text. The methods may be applied to lexical unit selection for speech recognition and also other segmentation tasks.