Abstract
In this paper we present a meeting state recognizer based on a combination of multi-modal sensor data in a smart room. Our approach is based on the training of a statistical model to use semantical cues generated by perceptual components. These perceptual components generate these cues in processing the output of one or multiple sensors. The presented recognizer is designed to work with an arbitrary combination of multi-modal input sensors. We have defined a set of states representing both meeting and non-meeting situations, and a set of features we base our classification on. Thus, we can model situations like presentation or break which are important information for many applications. We have hand-annotated a set of meeting recordings to verify our statistical classification, as appropriate multi-modal corpora are currently very sparse. We have also used several statistical classification methods for the best classification, which we validated on the hand-annotated corpus of real meeting data.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Banerjee, S., Rudnicky, A.I.: Using simple speech-based features to detect the state of a meeting and the roles of the meeting participants. In: Proceedings of ICSLP 2004, Jeju Island, Korea (2004)
Hakeem, A., Shah, M.: Ontology and taxonomy collaborated framework for meeting classification. In: ICPR 2004. Proceedings of the 17th International Conference on Pattern Recognition (2004)
Wang, J., Chen, G., Kotz, D.: A meeting detector and its applications. NH, USA (2004)
Campbell, N., Suzuki, N.: Working with very sparse data to detect speaker and listener participation in a meetings corpus. In: Proceedings of Multimodal Behaviour Theory to Usable Models, Genova, Italy (2006)
Carletta, J., Ashby, S., Bourban, S., Flynn, M., Guillemot, M., Hain, T., Kadlec, J., Kasairos, V., Kraaij, W., Kronenthal, M., Lathoud, G., Lincoln, M., Lisowska, A., McCowan, I., Post, W., Reidsma, D., Wellner, P.: The AMI meeting corpus: a pre–anouncement. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 28–39. Springer, Heidelberg (2005)
McCowan, I., Gatica-Perez, D., Bengio, S., Lathoud, G., Barnard, M., Zhang, D.: Automatic analysis of multimodal group action in meetings. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 305–317 (2005)
Chen, L., Rose, R.T., Parrill, F., Han, X., Tu, J., Huang, Z., Harper, M., Quek, F., McNeill, D., Tuttle, R.: (VACE) multimodal meeting corpus. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 40–51. Springer, Heidelberg (2005)
Stiefelhagen, R., Bowers, R.: CLEAR (Classification of Events. MD, USA (2007), http://isl.ira.uka.de/clear07/
Danninger, M., Robles, E., Takayama, L., Wang, Q., Kluge, T., Nass, C., Stiefelhagen, R.: The connector service - predicting availability in mobile contexts. In: Renals, S., Bengio, S., Fiscus, J.G. (eds.) MLMI 2006. LNCS, vol. 4299, pp. 129–141. Springer, Heidelberg (2006)
Crowley, J.L., Coutaz, J., Rey, G., Reignier, P.: Perceptual components for context aware computing. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, Springer, Heidelberg (2002)
Fleury, P., Cuřín, J., Kleindienst, J.: SitCom - development platform for multimodal perceptual services. In: Marik, V., Vyatkin, V., Colombo, A.W. (eds.) HoloMAS 2007. LNCS (LNAI), vol. 4659, pp. 104–113. Springer, Heidelberg (2007)
Witten, I.H., Frank, E.: Data mining. Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cuřín, J., Fleury, P., Kleindienst, J., Kessl, R. (2008). Meeting State Recognition from Visual and Aural Labels. In: Popescu-Belis, A., Renals, S., Bourlard, H. (eds) Machine Learning for Multimodal Interaction. MLMI 2007. Lecture Notes in Computer Science, vol 4892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78155-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-78155-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78154-7
Online ISBN: 978-3-540-78155-4
eBook Packages: Computer ScienceComputer Science (R0)