Abstract
By using context-dependent concept language models, we can significantly improve the performance of the speech understanding component of a dialogue system. We use several different modelling methods, such as bigrams, concept set probability estimations using graph theory and combinations of these. The contexts are selected both manually and automatically using mutual information. Using context-dependent bigrams, with contexts selected by the clustering algorithm, we establish a relative increase in performance of 10% on the attribute error rate on data obtained through the currently operational Swiss automatic train timetable information system.
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