Language models beyond word strings

In this paper we want to show how n-gram language models can be used to provide additional information in automatic speech understanding systems beyond the pure word chain. This becomes important in the context of conversational dialogue systems that have to recognize and interpret spontaneous speech. We show how n-grams can: (1) help to classify prosodic events like boundaries and accents; (2) be extended to directly provide boundary information in the speech recognition phase; (3) help to process speech repairs; and (4) detect and semantically classify out-of-vocabulary words. The approaches can work on the best word chain or a word hypotheses graph. Examples and experimental results are provided from our own research within the EVAR information retrieval system and the VERBMOBIL speech-to-speech translation system.