Estimating response obligation in multi-party human-robot dialogues

When a robot interacts with users in public spaces, it receives various sounds, such as surrounding noises and users´ voices, and furthermore needs to interact with multiple people at the same time. In this paper, we present a machine learning-based method to estimate a response obligation, i.e., whether an input sound should be responded to by the robot or not. This enables the robot to reject not only noises but also monologues and user utterances toward other users. Our proposed method uses not only acoustic information but also users´ motions and postures during a sound segment as features. In addition, user behaviors after a sound segment are taken into account to exploit typical user behaviors in human-robot interaction; for example, a user often stands still when he/she speaks to a robot. Experimental results showed our proposed model significantly outperformed a baseline. We found that user behaviors both during and after sound segments are helpful for estimating the response obligation.