When the Game Gets Difficult, then it is Time for Mimicry

  • Vijay Solanki
  • Alessandro Vinciarelli
  • Jane Stuart-Smith
  • Rachel Smith
Chapter
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 48)

Abstract

The computing community shows significant interest for the detection of mimicry, one of the names designating the tendency of interacting people to converge towards common behavioural patterns. This work shows experiments where speaker verification techniques, originally designed to detect fraudulent attempts to imitate others, are used to automatically detect the phenomenon. Furthermore, the experiments show that mimicry tends to be more frequent when people deal with harder collaborative tasks, thus suggesting that one of the functions of the phenomenon is to make communication easier or more effective in case of difficulties.

Keywords

Mimicry Social Signal Processing Mixtures of Gaussians 

References

  1. 1.
    Aguilar, L., Downey, G., Krauss, R., Pardo, J., Lane, S., Bolger, N.: A dyadic perspective on speech accommodation and social connection: Both partners’ rejection sensitivity matters. J. Personal. (2015)Google Scholar
  2. 2.
    Babel, M.: Dialect divergence and convergence in New Zealand english. Lang. Soc. 39(04), 437–456 (2010)CrossRefGoogle Scholar
  3. 3.
    Babel, M.: Evidence for phonetic and social selectivity in spontaneous phonetic imitation. J. Phon. 40(1), 177–189 (2012)CrossRefGoogle Scholar
  4. 4.
    Babel, M., Bulatov, D.: The role of fundamental frequency in phonetic accommodation. Lang. Speech 55(2), 231–248 (2012)CrossRefGoogle Scholar
  5. 5.
    Baker, R., Hazan, V.: DiapixUK: task materials for the elicitation of multiple spontaneous speech dialogs. Behav. Res. Methods 43(3), 761–770 (2011)CrossRefGoogle Scholar
  6. 6.
    Bimbot, F., Bonastre, J.F., Fredouille, C., Gravier, G., Magrin-Chagnolleau, I., Meignier, S., Merlin, T., Ortega-García, J., Petrovska-Delacrétaz, D., Reynolds, D.: A tutorial on text-independent speaker verification. EURASIP J. Appl. Signal Process. 2004, 430–451 (2004)CrossRefGoogle Scholar
  7. 7.
    Bousmalis, K., Mehu, M., Pantic, M.: Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases, and tools. Image Vis. Comput. 31(2), 203–221 (2013)CrossRefGoogle Scholar
  8. 8.
    Bulatov, D.: The effect of fundamental frequency on phonetic convergence. Phonology lab annual report, University of California at Berkeley (2009)Google Scholar
  9. 9.
    Burgoon, J.K., Stern, L.A., Dillman, L.: Interpersonal Adaptation: Dyadic interaction patterns. Cambridge University Press, Cambridge (1995)CrossRefGoogle Scholar
  10. 10.
    Chetouani, M.: Role of inter-personal synchrony in extracting social signatures: Some case studies. In: Proceedings of the Workshop on Roadmapping the Future of Multimodal Interaction Research Including Business Opportunities and Challenges, pp. 9–12 (2014)Google Scholar
  11. 11.
    Delaherche, E., Chetouani, M., Mahdhaoui, A., Saint-Georges, C., Viaux, S., Cohen, D.: Interpersonal synchrony: a survey of evaluation methods across disciplines. IEEE Trans. Affect. Comput. 3(3), 349–365 (2012)CrossRefGoogle Scholar
  12. 12.
    Giles, H., Coupland, J., Coupland, N.: Contexts of Accommodation: Developments in Applied Sociolinguistics. Cambridge University Press, Cambridge (1991)CrossRefGoogle Scholar
  13. 13.
    Henry, K., Sonderegger, M., Keshet, J.: Automatic measurement of positive and negative voice onset time. In: Proceedings of Interspeech (2012)Google Scholar
  14. 14.
    Jayagopi, D., Hung, H., Yeo, C., Gatica-Perez, D.: Modeling dominance in group conversations using nonverbal activity cues. IEEE Trans. Audio Speech Lang. Process. 17(3), 501–513 (2009)CrossRefGoogle Scholar
  15. 15.
    Pardo, J.S.: Reflections on phonetic convergence: speech perception does not mirror speech production. Lang. Linguist. Compass 6(12), 753–767 (2012)CrossRefGoogle Scholar
  16. 16.
    Purnell, T.C.: Convergence and contact in milwaukee: evidence from select african american and white vowel space features. J. Lang. Soc. Psychol. 28(4), 408–427 (2009)CrossRefGoogle Scholar
  17. 17.
    Salamin, H., Vinciarelli, A.: Automatic role recognition in multiparty conversations: an approach based on turn organization, prosody, and conditional random fields. IEEE Trans. Multimed. 14(2), 338–345 (2012)CrossRefGoogle Scholar
  18. 18.
    Schuller, B., Batliner, A.: Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing. Wiley (2013)Google Scholar
  19. 19.
    Tobin, S.: Phonetic accommodation in spanish-english and korean-english bilinguals. In: Proceedings of Meetings on Acoustics, vol. 19, p. 060087 (2013)Google Scholar
  20. 20.
    Vinciarelli, A., Pantic, M., Bourlard, H.: Social Signal Processing: survey of an emerging domain. Image Vis. Comput. J. 27(12), 1743–1759 (2009)CrossRefGoogle Scholar
  21. 21.
    Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D’Errico, F., Schroeder, M.: Bridging the gap between social animal and unsocial machine: a survey of Social Signal Processing. IEEE Trans. Affect. Comput. 3(1), 69–87 (2012)CrossRefGoogle Scholar
  22. 22.
    Zeng, Z., Pantic, M., Roisman, G., Huang, T.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vijay Solanki
    • 1
  • Alessandro Vinciarelli
    • 1
  • Jane Stuart-Smith
    • 1
  • Rachel Smith
    • 1
  1. 1.University of GlasgowGlasgowUK

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