Combining acoustic and pragmatic features to predict recognition performance in spoken dialogue systems

We use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-best recognition hypotheses to a spoken dialogue system. Our best results show a 25% weighted f-score improvement over a baseline system that implements a "grammar-switching" approach to context-sensitive speech recognition.