Verbal Conversation System for a Socially Embedded Robot Partner using Emotional Model

This paper proposes a verbal conversation system for a robot partner using emotional model. The robot partner calculates its emotional state based on the utterance sentence of the human. Then, the robot partner can control its utterance sentence based on the emotional parameters. As a results, the robot partner can interact with human emotionally naturally. In this paper, we explain the three parts of the conversation system's structure. The first part is time dependent selection based on the database contents. In this mode, the robot tells timely important contents, for example schedules. The mood parameter is used to change the sentence in this mode. The second component is utterance flow learning to select the utterance contents. The robot selects utterance sentence based on the utterance flow information and using its mood value as well. The third component is sentence building based on predefined rules. The rules include personality model of the robot partner. In this paper, we use emotional parameters based on the human sentences to make a natural communication system. Finally, we show experimental results of the proposed method, and conclude the paper. The future research for improving the robot partner system is discussed as well.