A framework for improving error detection and correction in spoken dialog systems

Despite the recent improvements in performance and reliably of the different components of dialog systems, it is still crucial to devise strategies to avoid error propagation from one another. In this paper, we contribute a framework for improved error detection and correction in spoken conversational interfaces. The framework combines user behavior and error modeling to estimate the probability of the presence of errors in the user utterance. This estimation is forwarded to the dialog manager and used to compute whether it is necessary to correct possible errors. We have designed an strategy differentiating between the main misunderstanding and non-understanding scenarios, so that the dialog manager can provide an acceptable tailored response when entering the error correction state. As a proof of concept, we have applied our proposal to a customer support dialog system. Our results show the appropriateness of our technique to correctly detect and react to errors, enhancing the system performance and user satisfaction.