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International Conference on Text, Speech, and Dialogue

TSD 2018: Text, Speech, and Dialogue pp 518–526Cite as

Classification of Formal and Informal Dialogues Based on Emotion Recognition Features

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11107))

Abstract

Social context is an important part of human communication, hence it is also important for improved human computer interaction. One aspect of social context is the level of formality. Here, motivated by the difference observed between the emotional annotation of formal and informal dialogues in the HuComTech corpus, we introduce a content-free classification scheme based on feature sets designed for emotion recognition. With this method we attain an error rate of \(8.8\%\) in the classification of formal and informal dialogues, which means a relative error rate reduction of more than \(40\%\) compared to earlier results. By combining our proposed method with earlier models, we were able to further reduce the error rate to below \(7\%\).

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Acknowledgements

The research reported in the paper was conducted with the support of the Hungarian Scientific Research Fund (OTKA) grant #K116938.

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Authors and Affiliations

  1. MTA Research Institute for Linguistics, Budapest, Hungary

    György Kovács

  2. MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary

    György Kovács

Authors
  1. György Kovács
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Correspondence to György Kovács .

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Editors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Aleš Horák

  3. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Ivan Kopeček

  4. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Karel Pala

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Kovács, G. (2018). Classification of Formal and Informal Dialogues Based on Emotion Recognition Features. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_56

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  • DOI: https://doi.org/10.1007/978-3-030-00794-2_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00793-5

  • Online ISBN: 978-3-030-00794-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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