Paper: User Simulations for Context-Sensitive Speech Recognition in Spoken Dialogue Systems

ACL ID E09-1058
Title User Simulations for Context-Sensitive Speech Recognition in Spoken Dialogue Systems
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

We use a machine learner trained on a combination of acoustic and contextual features to predict the accuracy of incom- ing n-best automatic speech recognition (ASR) hypotheses to a spoken dialogue system (SDS). Our novel approach is to use a simple statistical User Simulation (US) for this task, which measures the likelihood that the user would say each hypothesis in the current context. Such US models are now common in machine learning approaches to SDS, are trained on real dialogue data, and are related to the- ories of “alignment” in psycholinguistics. We use a US to predict the user’s next dia- logue move and thereby re-rank n-best hy- potheses of a speech recognizer for a cor- pus of 2564 user utterances. The method achieved a significant relative reduction of Word Error Rate (...