Initial language models for spoken dialogue systems

The estimation of initial language models for new applications of spoken dialogue systems without large taskspecific training corpora is becoming an increasingly important issue. This paper investigates two different approaches in which the task-specific knowledge contained in the language understanding grammar is exploited in order to generate n-gram language models for the speech recognizer: The first uses class-based language models for which the word-classes are automatically derived from the grammar. In the second approach, language models are estimated on artificial corpora which have been created from the understanding grammar. The application of fill-up techniques allows the combination of the strengths of both approaches and leads to a language model which shows optimal performance regardless of the amount of training data available. Perplexities and word error rates are reported for two different domains.