Bayesian networks for spoken dialogue management in multimodal systems of tour-guide robots

In this paper, we propose a method based on Bayesian networks for interpretation of multimodal signals used in the spoken dialogue between a tour-guide robot and visitors in mass exhibition conditions. We report on experiments interpreting speech and laser scanner signals in the dialogue management system of the autonomous tour-guide robot RoboX, successfully deployed at the Swiss National Exhibition (Expo.02). A correct interpretation of a user’s (visitor’s) goal or intention at each dialogue state is a key issue for successful voice-enabled communication between tourguide robots and visitors. To infer the visitors’ goal under the uncertainty intrinsic to these two modalities, we introduce Bayesian networks for combining noisy speech recognition with data from a laser scanner, which is independent of acoustic noise. Experiments with real data, collected during the operation of RoboX at Expo.02 demonstrate the effectiveness of the approach.