skip to main content
10.1145/2993148.2997617acmconferencesArticle/Chapter ViewAbstractPublication Pagesicmi-mlmiConference Proceedingsconference-collections
short-paper

Prediction/Assessment of communication skill using multimodal cues in social interactions

Published:31 October 2016Publication History

ABSTRACT

Understanding people’s behavior in social interactions is a very interesting problem in Social Computing. In this work, we automatically predict the communication skill of a person in various kinds of social interactions. We consider in particular, 1) Interview-based interactions - asynchronous interviews (web-based interview) Vs. synchronous interviews (regular face-to-face interviews) and 2) Non-interview based interactions - dyad and triad conversations (group discussions). We automatically extract multimodal cues related to verbal and non-verbal behavior content of the interaction. First, in interview-based interactions, we consider previously uninvestigated scenarios of comparing the participant’s behavioral and perceptual changes in the two contexts. Second, we address different manifestations of communication skill in different settings (face-to-face interaction vs. group). Third, the non-interview based interactions also leads to answer research questions such as “the relation between a good communicator and other group variables like dominance or leadership” Finally we look at several attributes (manually annotated) and features/feature groups (automatically extracted) that predicts communication skill well in all settings.

References

  1. Hassle-free Efficient Hiring. https://www.talview.com/automated-video.Google ScholarGoogle Scholar
  2. Batrinca, L. M. Multimodal personality recognition from audiovisual data. In Diss. University of Trento (2013).Google ScholarGoogle Scholar
  3. Biel, J. I., Teijeiro-Mosquera, L., and Gatica-Perez, D. Facetube: predicting personality from facial expressions of emotion in online conversational video. In Proceedings of the 14th ACM int. COnf. on Multimodal interaction, ACM (2012), 53–56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Celiktutan, O., Sariyanidi, E., and Gunes, H. Let me tell you about your personality!: Real-time personality prediction from nonverbal behavioural cues. In IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition (FG), 2015, vol. 1, IEEE (2015), 1–1.Google ScholarGoogle ScholarCross RefCross Ref
  5. Giannakopoulos, T., and Pikrakis, A. Introduction to Audio Analysis: A MATLAB ® Approach. Academic Press, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Hoque, M. E., Courgeon, M., Martin, J.-C., Mutlu, B., and Picard, R. W. Mach: My automated conversation coach. In ACM Int. Joint Conf. on Pervasive and ubiquitous computing, ACM (2013), 697–706. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hung, H., Jayagopi, D. B., Yeo, C., Friedland, G., Ba, S. O., Odobez, J.-M., Ramchandran, K., Mirghafori, N., and Gatica-Perez, D. Using audio and video features to classify the most dominant person in a group meeting. In Proc. ACM Int. Conf. on Multimedia (ACM MM) (Augsburg, Sep. 2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Keith, A., André, E., Baur, T., Bernardini, S., Chollet, M., Chryssafidou, E., Damian, I., Ennis, C., Egges, A., Gebhard, P., et al. The TARDIS framework: intelligent virtual agents for social coaching in job interviews. In Advances in Computer Entertainment. Springer, 2013, 476–491.Google ScholarGoogle Scholar
  9. Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., and Bartlett, M. The computer expression recognition toolbox (cert). In Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on, IEEE (2011), 298–305.Google ScholarGoogle Scholar
  10. Naim, I., Tanveer, M. I., Gildea, D., and Hoque, M. E. Automated prediction and analysis of job interview performance: The role of what you say and how you say it. Automatic Face and Gesture Recognition (FG) (2015).Google ScholarGoogle Scholar
  11. Nguyen, L. S., and Gatica-Perez, D. I would hire you in a minute: Thin slices of nonverbal behavior in job interviews. In ACM Int. Conf. on Multimodal Interaction, ACM (2015), 51–58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Rosenberg, A., and Hirschberg, J. Acoustic/prosodic and lexical correlates of charismatic speech. 513–516.Google ScholarGoogle Scholar
  13. Sanchez-Cortes, D., Aran, O., Mast, M. S., and Gatica-Perez, D. A nonverbal behavior approach to identify emergent leaders in small groups. Multimedia, IEEE Transactions on 14, 3 (2012), 816–832. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Son, N. L. Hirability in the wild: Analysis of online conversational video resumes. vol. 18 (2016).Google ScholarGoogle Scholar
  15. Spitzberg, B. H., and Adams, T. W. CSRS, the conversational skills rating scale: an instructional assessment of interpersonal competence. NCA, National Communication Association, 2007.Google ScholarGoogle Scholar
  16. Teijeiro-Mosquera, L., Biel, J.-I., Alba-Castro, J. L., and Gatica-Perez, D. What your face vlogs about: Expressions of emotion and big-five traits impressions in youtube.Google ScholarGoogle Scholar
  17. Weninger, F., Krajewski, J., Batliner, A., and Schuller, B. The voice of leadership: Models and performances of automatic analysis in online speeches. IEEE Transactions on Affective Computing 3, 4 (2012), 496–508. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Zechner, K., and Bejar, I. I. Towards automatic scoring of non-native spontaneous speech. Conf. on human language technology of the North American chapter of the association of computational linguistics. (2006), 216–223. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Prediction/Assessment of communication skill using multimodal cues in social interactions

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal Interaction
          October 2016
          605 pages
          ISBN:9781450345569
          DOI:10.1145/2993148

          Copyright © 2016 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 31 October 2016

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • short-paper

          Acceptance Rates

          Overall Acceptance Rate453of1,080submissions,42%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader