In order to obtain an understanding of the user’s emotion in human-machine dialogues, an analysis of dialogical utterances in the real world was performed. This work comprises three major steps. (1) The actual conditions of 16 basic emotions were evaluated using Japanese child voices, which were collected through the field test of the public spoken dialogue system. (2) Two factors were derived by a factor analysis. The factors were defined as fundamental psychological factors representing “delightful” and “hateable” emotions. (3) The relationships between the factors and the physical acoustic features were investigated to establish a capability to sense a user’s mental state for the dialogue system. In the experimental discriminations between the delightful and hateable emotions, a correct rate of 98.8% was achieved in classifying child’s utterances by the SVM (Support Vector Machine) with 11 acoustic features. Index Terms: emotional speech, classification, factor analysis, dialogue system, real world
['Ryuichi Nisimura', 'Souji Omae', 'Hideki Kawahara', 'Toshio Irino']
Analyzing Dialogue Data for Real-World Emotional Speech Classification