Abstract
We tackle a novel problem to predict how likely a humanoid robot is to be talked by a user. A human speaker usually takes his/her addressee's state into consideration and chooses when to talk to the addressee; this convention can be used when a system interprets its audio input. We formulate this problem by using machine learning whose input features are a humanoid's behaviors such as its posture, motion, and utterrance. A possible application of the model is to reject environmental noises that occur at timing when a cooperative user hardly talks to a robot.
