Automatic Dominance Detection in Meetings using easily obtainable features

R.J. Rienks; Dirk K.J. Heylen

doi:10.1007/11677482_7

Automatic Dominance Detection in Meetings using easily obtainable features

R.J. Rienks, Dirk K.J. Heylen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

43 Citations (Scopus)

56 Downloads (Pure)

Abstract

We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.

Original language	Undefined
Title of host publication	Revised Selected Papers of the 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms MLMI 2005
Editors	H. Bourlard, S. Renals
Place of Publication	Berlin
Publisher	Springer
Pages	76-86
Number of pages	11
ISBN (Print)	978-3-540-32549-9
DOIs	https://doi.org/10.1007/11677482_7
Publication status	Published - 2006
Event	2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005 - Edinburgh, United Kingdom Duration: 11 Jul 2005 → 13 Jul 2005 Conference number: MLMI

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Number	10
Volume	3869

Workshop

Workshop	2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005
Country	United Kingdom
City	Edinburgh
Period	11/07/05 → 13/07/05

Keywords

HMI-MI: MULTIMODAL INTERACTIONS
EC Grant Agreement nr.: FP6/506811
IR-66668
METIS-237665
EWI-8309

Access to Document

10.1007/11677482_7

MLMI2005
open
MLMI2005
open

Cite this

Rienks, R. J., & Heylen, D. K. J. (2006). Automatic Dominance Detection in Meetings using easily obtainable features. In H. Bourlard, & S. Renals (Eds.), Revised Selected Papers of the 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms MLMI 2005 (pp. 76-86). [10.1007/11677482_7] (Lecture Notes in Computer Science; Vol. 3869, No. 10). Springer. https://doi.org/10.1007/11677482_7

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abstract = "We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.",

keywords = "HMI-MI: MULTIMODAL INTERACTIONS, EC Grant Agreement nr.: FP6/506811, IR-66668, METIS-237665, EWI-8309",

author = "R.J. Rienks and Heylen, {Dirk K.J.}",

note = "10.1007/11677482_7 ; null ; Conference date: 11-07-2005 Through 13-07-2005",

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doi = "10.1007/11677482_7",

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isbn = "978-3-540-32549-9",

series = "Lecture Notes in Computer Science",

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Rienks, RJ & Heylen, DKJ 2006, Automatic Dominance Detection in Meetings using easily obtainable features. in H Bourlard & S Renals (eds), Revised Selected Papers of the 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms MLMI 2005., 10.1007/11677482_7, Lecture Notes in Computer Science, no. 10, vol. 3869, Springer, Berlin, pp. 76-86, 2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005, Edinburgh, United Kingdom, 11/07/05. https://doi.org/10.1007/11677482_7

Automatic Dominance Detection in Meetings using easily obtainable features. / Rienks, R.J.; Heylen, Dirk K.J.

Revised Selected Papers of the 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms MLMI 2005. ed. / H. Bourlard; S. Renals. Berlin : Springer, 2006. p. 76-86 10.1007/11677482_7 (Lecture Notes in Computer Science; Vol. 3869, No. 10).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Heylen, Dirk K.J.

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N2 - We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.

AB - We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.

KW - HMI-MI: MULTIMODAL INTERACTIONS

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KW - METIS-237665

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Rienks RJ, Heylen DKJ. Automatic Dominance Detection in Meetings using easily obtainable features. In Bourlard H, Renals S, editors, Revised Selected Papers of the 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms MLMI 2005. Berlin: Springer. 2006. p. 76-86. 10.1007/11677482_7. (Lecture Notes in Computer Science; 10). https://doi.org/10.1007/11677482_7