Titlebook: Machine Learning for Multimodal Interaction; 4th International Wo Andrei Popescu-Belis,Steve Renals,Hervé Bourlard Conference proceedings 2

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书目名称Machine Learning for Multimodal Interaction影响因子(影响力)




书目名称Machine Learning for Multimodal Interaction影响因子(影响力)学科排名




书目名称Machine Learning for Multimodal Interaction网络公开度




书目名称Machine Learning for Multimodal Interaction网络公开度学科排名




书目名称Machine Learning for Multimodal Interaction被引频次




书目名称Machine Learning for Multimodal Interaction被引频次学科排名




书目名称Machine Learning for Multimodal Interaction年度引用




书目名称Machine Learning for Multimodal Interaction年度引用学科排名




书目名称Machine Learning for Multimodal Interaction读者反馈




书目名称Machine Learning for Multimodal Interaction读者反馈学科排名

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演绎

An Ego-Centric and Tangible Approach to Meeting Indexing and Browsing presents our approach in the context of meeting and details our methods to bridge the gap between meeting data and personal information. Finally the article reports the progress of the integration of this approach within Fribourg’s meeting room.

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Towards an Objective Test for Meeting Browsers: The BET4TQB Pilot Experiments annotation-based search functionality is highly relevant, in particular keyword search over the meeting transcript. Two knowledge-poorer browsers appear to offer lower precision but higher speed. The BET task-based evaluation method thus appears to be a coherent measure of browser quality.

Aggressive

Face Recognition in Smart Roomscognition and analyze the effect of frame weighting. Experimental results obtained on the CHIL database, which has been collected from different smart rooms, show that benefiting from multi-view video data and handling registration errors reduce the false identification rates significantly.

无政府主义者

Automatic Labeling Inconsistencies Detection and Correction for Sentence Unit Segmentation in Converected by the system and observe that only 13% of them are labeling inconsitencies, while the rest are errors done by the classifier. The errors naturally cluster into 5 main classes for each of which we give hints on how the system can be improved to avoid these mistakes.

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猛烈责骂

Meeting State Recognition from Visual and Aural Labelsistical 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.

chronology

Automatic Annotation of Dialogue Structure from Simple User Interactione relative utility of various modes of user interaction and techniques for their interpretation. We show that performance improvements are possible, even with interfaces that demand very little of their users’ attention.