Predicting When People Will Speak to a Humanoid Robot

Abstract

We tackle the novel problem of predicting when a user is likely to begin speaking to a humanoid robot. Human speakers usually take the state of their addressee into consideration and choose when to begin speaking to the addressee, and our idea is to use this convention with a system that interprets audio input. The proposed method predicts when a user is likely to begin speaking to a humanoid robot by machine learning that uses the robot’s behaviors—such as its posture, motion, and utterance—as input features. We create a data set manually annotated by three human participants indicating in real time whether or not they would be likely to begin speaking to the robot. We collect the parts to which the three commonly give the same labels and use these parts as the training and evaluation data for machine learning. Results of an experimental evaluation showed that our model correctly predicted 88.5% of the common parts in an open test. This result is similar to the results of a cross-validation, demonstrating that our model is not dependent on a specific training data set. A possible application of the model is the elimination of environmental noises that occur at timing when a cooperative user is not likely to begin speaking to a robot.