Estimating Response Obligation by User Behaviors for Multi-Party Dialogue Robot

Abstract

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. If it incorrectly determines whether it should respond to these sounds, it will erroneously respond to surrounding noises or ignores user utterances toward it. 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 monologues and user utterances toward other users as well as noises. Our method uses not only acoustic information but also users' motions and postures during the input sound and user behaviors after the input sound as features. We demonstrate the new features significantly improved the estimation performance. We also investigate performances with various combinations of features and reveal that input sound classification results and a user's whole body motion are helpful for the estimation.