Error Awareness and Recovery in Task-Oriented Spoken Dialogue Systems

A persistent and important problem in spoken language interfaces is their lack of robustness when faced with understanding errors. The problem is present across all domains and interaction types, and stems primarily from the unreliability of the speech recognition process. I propose to alleviate this problem by (1) endowing spoken dialogue systems with better error awareness, (2) constructing a richer repertoire of error recovery strategies, and (3) developing a practical data-driven approach for making error handling decisions. The proposed work will address questions and make contributions in each of these three areas. For the first part, I propose to develop a belief updating mechanism that integrates confidence annotation and correction detection into a unified framework, and allows spoken dialogue systems to continuously track the reliability of the information they use. For the second part, I propose to implement and investigate an extended set of error recovery strategies addressing common problems in human-computer dialogue. Finally, I plan to bring these two capabilities together in a scalable reinforcement-learning based approach for making error handling decisions in task-oriented spoken dialogue systems.