Automatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coder

Mihalis, NA; Gunes, H; Pantic, M

Automatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coder

Mihalis, NA Gunes, H Pantic, M

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Publisher:: Multimodal Corpora
Publication Type:: Conference Proceeding
Citation:: Proceedings - Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality, 2010, pp. 43 - 48
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Mihalis, NA	en_US
dc.contributor.author	Gunes, H	en_US
dc.contributor.author	Pantic, M	en_US
dc.contributor.editor	Kipp, M	en_US
dc.contributor.editor	Martin, JC	en_US
dc.contributor.editor	Paggio, P	en_US
dc.contributor.editor	Heylen, D	en_US
dc.date	2010-05-17	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Proceedings - Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality, 2010, pp. 43 - 48	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16106
dc.description.abstract	This paper focus on automatic of spontaneous data using continuous dimensional labels from multiple coders. It introduces efficient algorithms to the aim of (i) producing ground-truth by maximizing inter-coder agreement, (ii) eliciting the frames or samples that capture the transition to and from an emotional state, and (iii) automatic segmentation of spontaneous audio-visual data to be used by machine learning techniques that cannot handle unsegmented sequences. As a proof of concept, the algorithms introduced are tested using data annotated in arousal and valence space. However, they can be straighforawardly applied to data annotated in other continuous emotional spaces, such as power and expectation.	en_US
dc.publisher	Multimodal Corpora	en_US
dc.relation.ispartof	Proceedings - Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality	en_US
dc.relation.ispartof	Multimodal Corpora: Advances in Capturing, Coding and Analyzing Multimodality	en_US
dc.title	Automatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coder	en_US
dc.type	Conference Proceeding
utslib.location	Valletta, Malta	en_US
utslib.location.activity	Valletta, Malta	en_US
utslib.for	080401 Coding and Information Theory	en_US
dc.location.activity	Valletta, Malta	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Valletta, Malta	en_US
pubs.start-date	2010-05-17	en_US

Abstract:

This paper focus on automatic of spontaneous data using continuous dimensional labels from multiple coders. It introduces efficient algorithms to the aim of (i) producing ground-truth by maximizing inter-coder agreement, (ii) eliciting the frames or samples that capture the transition to and from an emotional state, and (iii) automatic segmentation of spontaneous audio-visual data to be used by machine learning techniques that cannot handle unsegmented sequences. As a proof of concept, the algorithms introduced are tested using data annotated in arousal and valence space. However, they can be straighforawardly applied to data annotated in other continuous emotional spaces, such as power and expectation.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16106