Predicting Concept Types in User Corrections in Dialog

Most dialog systems explicitly confirm user-provided task-relevant concepts. User responses to these system confirmations (e.g. corrections, topic changes) may be misrecognized because they contain unrequested task-related concepts. In this paper, we propose a concept-specific language model adaptation strategy where the language model (LM) is adapted to the concept type(s) actually present in the user’s post-confirmation utterance. We evaluate concept type classification and LM adaptation for post-confirmation utterances in the Let’s Go! dialog system. We achieve 93% accuracy on concept type classification using acoustic, lexical and dialog history features. We also show that the use of concept type classification for LM adaptation can lead to improvements in speech recognition performance.