Discourse Particles and User Characteristics in Naturalistic Human-Computer Interaction

  • Ingo Siegert
  • Matthias Haase
  • Dmytro Prylipko
  • Andreas Wendemuth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8511)


In human-human interaction (HHI) the behaviour of the speaker is amongst others characterised by semantic and prosodic cues. These short feedback signals minimally communicate certain dialogue functions such as attention, understanding or other attitudinal reactions. Human-computer interaction (HCI) systems have failed to note and respond to these details so far, resulting in users trying to cope with and adapt to the machines behaviour. In order to enhance HCI, an adaptation to the user’s behaviour, individual skills, and the integration of a general human behaviour understanding is indispensable. Another issue is the question if the usage of feedback signals is influenced by the user’s individuality. In this paper, we investigate the influence of specific feedback signals, known as discourse particles (DPs), with communication style and psychological characteristics within a naturalistic HCI. This investigation showed that there is a significant difference in the usage of DPs for users of certain user characteristics.


human-machine-interaction discourse particles personality user characteristics 


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  1. 1.
    Allwood, J., Nivre, J., Ahlsn, E.: On the semantics and pragmatics of linguistic feedback. Journal of Semantics 9(1), 1–26 (1992)CrossRefGoogle Scholar
  2. 2.
    Anderson, A.A.: Predictors of computer anxiety and performance in information systems. Computers in Human Behavior 12(1), 61–77 (1996)CrossRefGoogle Scholar
  3. 3.
    Beckers, J.J., Rikers, R.M., Schmidt, H.G.: The influence of computer anxiety on experienced computer users while performing complex computer tasks. Computers in Human Behavior 22(3), 456–466 (2006)CrossRefGoogle Scholar
  4. 4.
    Ceyhan, E.: Computer anxiety of teacher trainees in the framework of personality variables. Computers in Human Behavior 22(2), 207–220 (2006)CrossRefGoogle Scholar
  5. 5.
    Corley, M., Stewart, O.W.: Hesitation Disfluencies in Spontaneous Speech: The Meaning of um. Language and Linguistics Compass 2, 589–602 (2008)CrossRefGoogle Scholar
  6. 6.
    Costa, P., McCrae, R.: NEO-PI-R Professional manual. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Inventory (NEO-FFI). Psychological Assessment Resources, Odessa (1992)Google Scholar
  7. 7.
    Cuperman, R., Ickes, W.: Big five predictors of behavior and perceptions in initial dyadic interactions: Personality similarity helps extraverts and introverts, but hurts disagreeables. J. Pers. Soc. Psychol. 97(4), 667–684 (2009)CrossRefGoogle Scholar
  8. 8.
    Daily, J.: Personality and Interpersonal Communication. In: Handbook of Interpersonal Communication, pp. 133–180. Sage, Thousand Oaks (2002)Google Scholar
  9. 9.
    Elliot, C.: The application of pragmatics in Human-Computer interaction. Ph.D. thesis, Sheffield Hallam University (1993)Google Scholar
  10. 10.
    Fischer, K., Wrede, B., Brindöpke, C., Johanntokrax, M.: Quantitative und funktionale Analysen von Diskurspartikeln im Computer Talk. International Journal for Language Data Processing 20(1-2), 85–100 (1996)Google Scholar
  11. 11.
    Frommer, J., Michaelis, B., Rsner, D., Wendemuth, A., Friesen, R., Haase, M., Kunze, M., Andrich, R., Lange, J., Panning, A., Siegert, I.: Towards Emotion and Affect Detection in the Multimodal LAST MINUTE Corpus. In: Proc. of the Eight LREC 2012, ELRA, Istanbul (May 2012)Google Scholar
  12. 12.
    Funder, D.C., Sneed, C.D.: Behavioral manifestations of personality: An ecological approach to judgmental accuracy. J. Pers. Soc. Psychol. 64(3), 479–490 (1993)CrossRefGoogle Scholar
  13. 13.
    Hermann, F., Niedermann, I., Peissner, M., Henke, K., Naumann, A.: Users interact differently: Towards a usability- oriented user taxonomy. In: Jacko, J.A. (ed.) HCI 2007. LNCS, vol. 4550, pp. 812–817. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Horowitz, L., Alden, L., Wiggins, J., Pincus A.: Inventory of Interpersonal Problems Manual. The Psychological Corporation, Odessa (2000)Google Scholar
  15. 15.
    Horowitz, L., Strau, B., Kordy, H.: Inventar zur Erfassung Interpersonaler Probleme (IIPD) (Inventory of interpersonal problems-German version), 2nd edn. Beltz, Weinheim (2000)Google Scholar
  16. 16.
    Howell, D.: Statistical Methods for Psychology, 7th edn. Cengage Learning (2009)Google Scholar
  17. 17.
    Jahnke, W., Erdmann, G., Kallus, K.: Stressverarbeitungsfragebogen mit SVF 120 und SVF 78, 3rd edn. Hogrefe, Göttingen (2002)Google Scholar
  18. 18.
    John, O., Hampson, S., Goldberg, L.: Is there a basic level of personality description? J. Pers. Soc. Psychol. 60(3), 348–361 (1991)CrossRefGoogle Scholar
  19. 19.
    King, J., Bond, T., Blandford, S.: An investigation of computer anxiety by gender and grade. Computers in Human Behavior 18(1), 69–84 (2002)CrossRefGoogle Scholar
  20. 20.
    Lange, J., Frommer, J.: Subjektives Erleben und intentionale Einstellung in Interviews zur Nutzer-Companion-Interaktion (Subjective experience and intentional setting within intervies of User-Companion-Interaction). In: Informatik 2011: Informatik schafft Communities, Beitrge der 41. Jahrestagung der GI. Lecture Notes in Informatics, vol. 192, pp. 240–254 (2011)Google Scholar
  21. 21.
    Lithari, C., Frantzidis, C., Papadelis, C., Vivas, A., Klados, M., Kourtidou-Papadeli, C., Pappas, C., Ioannides, A., Bamidis, P.: Are females more responsive to emotional stimuli? A neurophysiological study across arousal and valence dimensions. Brain Topography 23(1), 27–40 (2010)CrossRefGoogle Scholar
  22. 22.
    Naumann, A., Hermann, F., Peissner, M., Henke, K.: Interaktion mit Informations- und Kommunikationstechnologie: Eine Klassifikation von Benutzertypen (Interaction with information and communication technology: A classification of user types). In: Herczeg, M., Kindsmller, M.C. (eds.) Mensch und Computer 2008: Viel Mehr Interaktion, pp. 37–45. Oldenbourg Verlag, München (2008)Google Scholar
  23. 23.
    Ozer, D.J., Benet-Martinez, V.: Personality and the prediction of consequential outcomes. Annu. Rev. Psychol. 57(3), 401–421 (2006)CrossRefGoogle Scholar
  24. 24.
    Paschen, H.: Die Funktion der Diskurspartikel HM (The function of discourse particles HM). Master’s thesis, University Mainz (1995)Google Scholar
  25. 25.
    Prylipko, D., Rösner, D., Siegert, I., Günther, S., Friesen, R., Haase, M., Vlasenko, B., Wendemuth, A.: Analysis of significant dialog events in realistic human computer interaction. Journal on Multimodal User Interfaces (2013)Google Scholar
  26. 26.
    Rösner, D., Kunze, M., Otto, M., Frommer, J.: Linguistic analyses of the LAST MINUTE corpus. In: Jancsary, J. (ed.) Proceedings of KONVENS 2012, pp. 145–154. oral presentations, Main track (2012)Google Scholar
  27. 27.
    Schmidt, J.E.: Bausteine der Intonation (Components of intonation). In: Neue Wege der Intonationsforschung, Germanistische Linguistik, vol. 157-158, pp. 9–32. Georg Olms Verlag, Hildesheim (2001)Google Scholar
  28. 28.
    Siegert, I., Hartmann, K., Philippou-Hübner, D., Wendemuth, A.: Human Behaviour in HCI: Complex Emotion Detection through Sparse Speech Features. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds.) HBU 2013. LNCS, vol. 8212, pp. 246–257. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  29. 29.
    Siegert, I., Prylipko, D., Hartmann, K., Böck, R., Wendemuth, A.: Investigating the form-function-relation of the discourse particle ”hm” in a naturalistic human-computer interaction. In: Bassis, S., Esposito, A., Morabito, F.C. (eds.) Recent Advances of Neural Network Models and Applications, Smart Innovation, Systems and Technologies, vol. 26, pp. 387–394. Springer (2014)Google Scholar
  30. 30.
    Sullivan, H.: The interpersonal theory of psychiatry. Norton, New York (1953)Google Scholar
  31. 31.
    von Thun, F.S.: Miteinander reden 1 – Störungen und Klärungen. Allgemeine Psychologie der Kommunikation (Talking to Each Other 1 - faults and clarifications. General Psychology of Communication). Rowohlt, Reinbek (1981)Google Scholar
  32. 32.
    Van der Veer, G.C., Tauber, M.J., Waem, Y., Van Muylwijk, B.: On the interaction between system and user characteristics. Behaviour & Information Technology 4(4), 289–308 (1985)CrossRefGoogle Scholar
  33. 33.
    Weinberg, G.M.: The psychology of computer programming. Van Nostrand Reinhold, New York (1971)Google Scholar
  34. 34.
    Weiss, A., Mirnig, N., Buchner, R., Förster, F., Tscheligi, M.: Transferring human-human interaction studies to HRI scenarios in public space. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part II. LNCS, vol. 6947, pp. 230–247. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  35. 35.
    Wendemuth, A., Biundo, S.: A Companion Technology for Cognitive Technical Systems. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) COST 2102. LNCS, vol. 7403, pp. 89–103. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ingo Siegert
    • 1
  • Matthias Haase
    • 2
  • Dmytro Prylipko
    • 1
  • Andreas Wendemuth
    • 1
  1. 1.Institute for Information Technology and CommunicationsOtto von Guericke University MagdeburgGermany
  2. 2.Department of Psychosomatic Medicine and PsychotherapyOtto von Guericke University MagdeburgGermany

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