Considering cross-cultural context in the automatic recognition of emotions

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2015
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International Journal of Machine Learning and Cybernetics. 2015, 6(1), pp. 119-127. ISSN 1868-8071. eISSN 1868-808X. Available under: doi: 10.1007/s13042-013-0192-2
Zusammenfassung

Automatic recognition of emotions remains an ongoing challenge and much effort is being invested towards developing a system to solve this problem. Although several systems have been proposed, there is still none that considers the cultural context for emotion recognition. It remains unclear whether emotions are universal or culturally specific. A study on how culture influences the recognition of emotions is presented. For this purpose, a multicultural corpus for cross-cultural emotion analysis is constructed. Subjects from three different cultures—American, Asian and European—are recruited. The corpus is segmented and annotated. To avoid language artifacts, the emotion recognition model considers facial expressions, head movements, body motions and dimensional emotions. Three training and testing paradigms are carried out to compare cultural effects: intra-cultural, cross-cultural and multicultural emotion recognition. Intra-cultural and multicultural emotion recognition paradigms raised the best recognition results; cross-cultural emotion recognition rates were lower. These results suggest that emotion expression varies by culture, representing a hint of emotion specificity.

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Affect, Culture, Universality, Specificity, Emotional corpus
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ISO 690QUIRÓS-RAMÍREZ, M. Alejandra, Takehisa ONISAWA, 2015. Considering cross-cultural context in the automatic recognition of emotions. In: International Journal of Machine Learning and Cybernetics. 2015, 6(1), pp. 119-127. ISSN 1868-8071. eISSN 1868-808X. Available under: doi: 10.1007/s13042-013-0192-2
BibTex
@article{QuirosRamirez2015-02Consi-44415,
  year={2015},
  doi={10.1007/s13042-013-0192-2},
  title={Considering cross-cultural context in the automatic recognition of emotions},
  number={1},
  volume={6},
  issn={1868-8071},
  journal={International Journal of Machine Learning and Cybernetics},
  pages={119--127},
  author={Quirós-Ramírez, M. Alejandra and Onisawa, Takehisa}
}
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