Automated wizard-of-oz for spoken dialogue systems

Designing and building natural language spoken dialogue systems require large amounts of speech utterances, which adequately represent the intended human-machine dialogues. For this purpose, typically, first a “Wizard-of-Oz” data collection is performed, and then the collected data is transcribed and labeled by expert labelers. Finally, the data is used to train both the speech recognizer and the spoken language understanding stochastic models. Data collection and labeling is an expensive and time consuming manual process. In this paper we propose a completely Automated Wizard, which is capable of recognizing and understanding application independent requests reusing the previously labeled and transcribed data from similar domains, and improving the informativeness of the collected data. We demonstrate that, in the context of automated call routing, compared to the existing data collection systems, the Automated Wizard better captures the user intentions and produces substantially shorter interactions resulting in a better user experience and a less intrusive approach.