Authors:
Ryo Senuma
1
;
Sho Yokota
1
;
Akihiro Matsumoto
1
;
Daisuke Chugo
2
;
Satoshi Muramatsu
3
and
Hiroshi Hashimoto
4
Affiliations:
1
Dept. of Mechanical Engineering, Toyo University, Saitama, Japan
;
2
School of Engineering, Kwansei Gakuin University, Sanda, Japan
;
3
Dept. of Applied Computer Eng., Tokai University, Hiratsuka, Japan
;
4
Adv. Institute of Industrial Tech., Shinagawa, Japan
Keyword(s):
Machine Learning, Emotion Estimation, Support Vector Machine, Natural Utterances Voice, Acting Voice.
Abstract:
In social media, text information has a problem that it is difficult to convey the nuances and emotions to the other people. Moreover, manual texting is a time consuming task. Therefore, this research proposes a system that creates text information by voice input from the acoustic information, and automatically insert emoticons matching the user’s emotion. The proposed system is employed based on the eight basic emotions of Plutchik’s Wheel of Emotions. Two types of data: natural utterances voice and acting voice was applied to the SVM (Support Vector Machine) method in the experiment to estimate emotions. The result shows that the accuracy of natural utterances voice and acting voice are 30% and 70%, respectively.