Comparison of sensibilities of Japanese and Koreans in recognizing emotions from speech by using Bayesian networks

The paper describes a comparison of the sensibility of recognizing emotions from human voices speaking Japanese and Korean. Our study focuses on the emotional elements included in the human voice, and our method uses Bayesian networks of prosodic features as models of Japanese´s and Korean´s sensibilities in recognizing emotions. The training datasets are prosodic features extracted from emotionally expressive voice data in the two languages. Our method makes the Bayesian network learn the dependence and its strength between nonverbal voice features and its emotion. We compare the sensibilities of emotion recognition from Japanese and Koreans speech by examining the cross-inference through two Bayesian networks with speech in the other language.