LAUSR

Spoken dialog systems are employed in various devices to help users operate them. An advantage of a spoken dialog system is that the user can make input utterances freely, but the system sometimes makes it difficult for the user to speak to it. The system should estimate the state of a user who encounters a problem when starting a dialog and then give appropriate help before the user abandons the dialog. Based on this assumption, our research aims to construct a system which responds to a user who does not reply to the system. In this paper, we propose a method of discriminating the user’s state based on vector quantization of non-verbal information such as prosodic features, facial feature points, and gaze. The experimental results showed that the proposed method outperforms the conventional approaches and achieves a discrimination ratio of 72.0%. Then, we examined sequential discrimination for responding to the user at an appropriate timing. The results indicate that the discrimination ratio reached equal to the end of the session at around 6.0 s.