Expressing reactive emotion based on multimodal emotion recognition for natural conversation in human–robot interaction.

Human–human interaction consists of various nonverbal behaviors that are often emotion-related. To establish rapport, it is essential that the listener respond to reactive emotion in a way that makes sense given the speaker's emotional state. However, human–robot interactions generally fail in this regard because most spoken dialogue systems play only a question-answer role. Aiming for natural conversation, we examine an emotion processing module that consists of a user emotion recognition function and a reactive emotion expression function for a spoken dialogue system to improve human–robot interaction. For the emotion recognition function, we propose a method that combines valence from prosody and sentiment from text by decision-level fusion, which considerably improves the performance. Moreover, this method reduces fatal recognition errors, thereby improving the user experience. For the reactive emotion expression function, the system's emotion is divided into emotion category and emotion level, which are predicted using the parameters estimated by the recognition function on the basis of distributions inferred from human–human dialogue data. As a result, the emotion processing module can recognize the user's emotion from his/her speech, and expresses a reactive emotion that matches. Evaluation with ten participants demonstrated that the system enhanced by this module is effective to conduct natural conversation. [ABSTRACT FROM AUTHOR]