ISCA Archive Interspeech 2005
ISCA Archive Interspeech 2005

Context-sensitive statistical language modeling

Alexander Gruenstein, Chao Wang, Stephanie Seneff

We present context-sensitive dynamic classes - a novel mechanism for integrating contextual information from spoken dialogue into a class n-gram language model. We exploit the dialogue system's information state to populate dynamic classes, thus percolating contextual constraints to the recognizer's language model in real time. We describe a technique for training a language model incorporating context-sensitive dynamic classes which considerably reduces word error rate under several conditions. Significantly, our technique does not partition the language model based on potentially artificial dialogue state distinctions; rather, it accommodates both strong and weak expectations via dynamic manipulation of a single model.