Pattern recognition of emotion with neural network

Author

Yamada, Tomoaki ; Hashimoto, Hiroya ; Tosa, Naoko

Author_Institution

Inst. of Ind. Sci., Tokyo Univ.

Volume

1

fYear

1995

fDate

6-10 Nov 1995

Firstpage

183

Abstract

Proposes an emotion model for communication which also transfers personality and character information. The emotion model customizes to individual human communication partners by learning. Learning is achieved by neural networks converting input voice signals to an emotion state. The emotion state decides the response of the partner. The emotion state is divided into four categories: sadness; cheerfulness; happiness; and anger. For example, a loud voice causes the emotion of anger. This paper also discusses the emotion model as network agent between two human communication partners

Keywords

behavioural sciences; behavioural sciences computing; human factors; learning (artificial intelligence); neural nets; speech recognition; Network Neuro-Baby; anger; character information; cheerfulness; emotion model; happiness; human communication; input voice signals; learning; neural network; pattern recognition; personality; response; sadness; Character generation; Education; Emotion recognition; Face recognition; Humans; Monitoring; Neural networks; Pattern recognition; Petroleum; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-3026-9

Type

conf

DOI

10.1109/IECON.1995.483355

Filename

483355