Analysis and generation of laughter motions, and evaluation in an android robot

Laughter commonly occurs in daily interactions, and is not only simply related to funny situations, but also to expressing some type of attitudes, having important social functions in communication. The background of the present work is to generate natural motions in a humanoid robot, so that miscommunication might be caused if there is mismatching between audio and visual modalities, especially in laughter events. In the present work, we used a multimodal dialogue database, and analyzed facial, head, and body motion during laughing speech. Based on the analysis results of human behaviors during laughing speech, we proposed a motion generation method given the speech signal and the laughing speech intervals. Subjective experiments were conducted using our android robot by generating five different motion types, considering several modalities. Evaluation results showed the effectiveness of controlling different parts of the face, head, and upper body (eyelid narrowing, lip corner/cheek raising, eye blinking, head motion, and upper body motion control).