Evaluating model that predicts when people will speak to humanoid robot and handling variations by individuality and instruction

Abstract

We have tackled a novel problem of predicting when a user is likely to begin speaking to a humanoid robot. The generality of the prediction model should be examined to apply it to various users. We show in this paper that the following two empirical evaluations. First, our proposed model does not depend on the specific participants whose data were used in our previous experiment. Second, the model can handle variations caused by individuality and instruction. We collect a data set to which 25 human participants give labels, indicating whether or not they would be likely to begin speaking to the robot. We then train a new model with the collected data and verify its performance by cross validation and open tests. We also investigate relationship of how much each human participant felt possible to begin speaking with a model parameter and instruction given to them. This shows a possibility of our model to handle such variations.