The Utility of Semantic-Pragmatic Information and Dialogue-State for Speech Recognition in Spoken Dialogue Systems

Information about the dialogue-state can be integrated into language models to improve performance of the speech recognizer in a dialogue system. A dialogue state is defined in this paper as the question, the user is replying to. One of the main problems in dialogue-state dependent language modelling is the limitation of training data. In order to obtain robust models, we use the method of rational interpolation to smooth between a dialogue-state dependent and a general language model. In contrast to linear interpolation methods, rational interpolation weights the different predictors according to their reliability. Semantic-pragmatic knowledge is used to enlargen the training data of the language models. Both methods reduce perplexity and word error rate significantly