Using Machine-learning Feedback of Laughter Judgments to Reduce Tension in Performers

Abstract

To alleviate the tension that performers feel during a performance, we proposed a system that judges the audience’s laughter in real-time using machine learning and conveys the feedback to the performers. The conventional threshold-based laughter judgment was insufficient to alleviate tension.Therefore, this study adopted a method to accurately judge laughter using machine learning and reduce stress by providing vibrational feedback. In this experiment, changes in the tension level of a comedic comic performer were evaluated using the system, and a statistically significant tension-relieving effect was obtained. The questionnaire results also suggested areas for improvement, such as the usefulness of visual feedback. Besides, a comparison of the actual laughter timing and the laughter judgment using machine learning showed that the system could recognize laughter at approximately 60% the exact timing.