Recognition of Affective Communicative Intent in Robot-Directed Speech

Human speech provides a natural and intuitive interface for both communicating with humanoid robots as well as for teaching them. In general, the acoustic pattern of speech contains three kinds of information: who the speaker is, what the speaker said, and how the speaker said it. This paper focuses on the question of recognizing affective communicative intent in robot-directed speech without looking into the linguistic content. We present an approach for recognizing four distinct prosodic patterns that communicate praise, prohibition, attention, and comfort to preverbal infants. These communicative intents are well matched to teaching a robot since praise, prohibition, and directing the robot's attention to relevant aspects of a task, could be used by a human instructor to intuitively facilitate the robot's learning process. We integrate this perceptual ability into our robot's “emotion” system, thereby allowing a human to directly manipulate the robot's affective state. This has a powerful organizing influence on the robot's behavior, and will ultimately be used to socially communicate affective reinforcement. Communicative efficacy has been tested with people very familiar with the robot as well as with naïve subjects.