Multi-Level Error Handling for Tree-Based Dialogue Course Management

For spoken dialogue systems, errors can occur on different levels of the system's architecture. One of the principal causes for errors during a dialogue session are erroneous recognition results which often lead to incorrect semantic interpretations. Even if the speech input signal has been correctly recognized, a natural language understanding component can produce errorprone sentence meanings due to the limitations of its underlying model. To cope with this problem, we introduce a multi-level error-detection mechanism based on several features in order to find erroneous recognitions, error-prone semantic interpretations as well as ambiguities, and contradictions. Here, the confidence output of one level directly serves as an additional input for the subsequent level. The proposed features and scoring criteria are passed to the dialogue manager which then determines the subsequent dialogue action.