Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

The automatic analysis of social interactions is attracting significant interest in the multimedia community. This work addresses one of the most important aspects of the problem, namely the recognition of roles in social exchanges. The proposed approach is based on Social Network Analysis, for the representation of individuals in terms of their interactions with others, and probabilistic sequential models, for the recognition of role sequences underlying the sequence of speakers in conversations. The experiments are performed over different kinds of data (around 90 hours of broadcast data and meetings), and show that the performance depends on how formal the roles are, i.e. on how much they constrain people behavior.

REFERENCES

• 1: J. Ajmera and C. Wooters. A robust speaker clustering algorithm. In Proc. of IEEE Workshop on Automatic Speech Recognition Understanding, pages 411--416, 2003.
• 2: Christopher M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 2006
• 3: J. Dines, J. Vepa, and T. Hain. The segmentation of multi-channel meeting recordings for automatic speech recognition. In Proc. of Interspeech, pages 1213--1216, 2006.
• 4: Sarah Favre , Hugues Salamin , John Dines , Alessandro Vinciarelli, Role recognition in multiparty recordings using social affiliation networks and discrete distributions, Proceedings of the 10th international conference on Multimodal interfaces, October 20-22, 2008, Chania, Crete, Greece [doi>10.1145/1452392.1452401]
• 5: J. Levine and R. Moreland. Small groups. In D. Gilbert and G. Lindzey, editors, The handbook of social psychology, volume 2, pages 415--469. Oxford University Press, 1998.
• 6: Yang Liu, Initial study on automatic identification of speaker role in broadcast news speech, Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX, p.81-84, June 04-09, 2006, New York, New York
• 7: F. Massey Jr. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, pages 68--78, 1951.
• 8: L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proc. of the IEEE, volume 77, pages 257--286, 1989.
• 9: R. Rosenfeld. Two decades of statistical language modeling: Where do we go from here. In Proceedings of the IEEE, volume 88, pages 1270--1278, 2000.
• 10: H. Tischler. Introduction to Sociology. Harcourt Brace College Publishers, 1990.
• 11: Alessandro Vinciarelli , Maja Pantic , Hervé Bourlard, Social signal processing: Survey of an emerging domain, Image and Vision Computing, v.27 n.12, p.1743-1759, November, 2009 [doi>10.1016/j.imavis.2008.11.007]
• 12: S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Experimentation

Keywords:
HMMS, role recognition, social network analysis, statistical language models