Towards user-independent classification of multimodal emotional signals

Author

Kim, Jonghwa ; André, Elisabeth ; Vogt, Thurid

Author_Institution

Augsburg Univ., Augsburg, Germany

fYear

2009

fDate

10-12 Sept. 2009

Firstpage

1

Lastpage

7

Abstract

Coping with differences in the expression of emotions is a challenging task not only for a machine, but also for humans. Since individualism in the expression of emotions may occur at various stages of the emotion generation process, human beings may react quite differently to the same stimulus. Consequently, it comes as no surprise that recognition rates reported for a user-dependent system are significantly higher than recognition rates for a user-independent system. Based on empirical data we obtained in our earlier work on the recognition of emotions from biosignals, speech and their combination, we discuss which consequences arise from individual user differences for automated recognition systems and outline how these systems could be adapted to particular user groups.

Keywords

emotion recognition; automated recognition systems; biosignals; emotion generation process; multimodal emotional signals; recognition rates; user independent classification; user-dependent system; user-independent system; Appraisal; Audio recording; Automatic speech recognition; Emotion recognition; Humans; Machine learning algorithms; Pattern recognition; Psychology; Speech analysis; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on

Conference_Location

Amsterdam

Print_ISBN

978-1-4244-4800-5

Electronic_ISBN

978-1-4244-4799-2

Type

conf

DOI

10.1109/ACII.2009.5349495

Filename

5349495