Error handling in a stochastic dialog system through confidence measures

In this work, we present an approach to take advantage of confidence measures obtained during the recognition and understanding processes of a dialog system, in order to guide the behavior of the dialog manager. Our approach allows the system to ask the user for confirmation about the data which have low confidence values associated to them, after the recognition or understanding processes. This technique could help to protect the system from recognition or understanding errors. Although the number of confirmation turns could increase, it would be less probable for the system to consider data with a low confidence value as correct. The understanding module and the dialog manager that we have used are modelled by stochastic automata, and some confidence measures are proposed for the understanding module. An evaluation of the behavior of the dialog system is also presented.