Language model adaptation for spoken language systems

In a human-machine interaction (dialog) the statistical language variations are large among different stages of the dialog and across different speakers. Moreover, spoken dialog systems require extensive training data for training adaptive language models. In this paper we address the problem of open-vocabulary language models allowing the user for any possible response at each stage of the dialog. We propose a novel off-line adaptation of stochastic language models effective for their generalization (openvocabulary) and selective (dialog context) properties. We outline the integration of the finite state dialog model and the language model adaptation algorithm. The performance of the speech recognition and understanding language models are evaluated with the Carmen Sandiego multimodal computer game. The new language models give an overall understanding error rate reduction of 44% over the baseline system.