Abstract
We address the problem of identifying when a child playing an interactive game in a small group is speaking to an animated or robotic character versus conferring with his friend. This judgment about addressee is critical for turn-taking. We explore a machine learning approach using a Support Vector Machine (SVM) to integrate audio and visual features that we believe can be sensed accurately. We extend the basic model by including a simple form of group information, limited speech recognition, and limited game state to improve classification accuracy. Our results demonstrate high accuracy in detecting when the character is being addressed. This model improves our understanding of children’s group behavior in interacting with an agent.
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