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When using spoken dialog systems in actual environments, users sometimes abandon the dialog without making any input utterance. To help these users before they give up, the system should know why they could not make an utterance. Thus, we have examined a method to estimate the state of a dialog user by capturing the user’s non-verbal behavior even when the user’s utterance is not observed. The proposed method is based on vector quantization of multi-modal features such as non-verbal speech, feature points of the face, and gaze. The histogram of the VQ code is used as a feature for determining the state. We call this feature “the Bagof-Behaviors.” According to the experimental results, we prove that the proposed method surpassed the results of conventional approaches and discriminated the target user’s states with an accuracy of more than 70%.

User Modeling by Using Bag-of-Behaviors for Building a Dialog System Sensitive to the Interlocutor's Internal State