Title
The AMI Meeting Corpus: A Pre-Announcement
Author
Carletta, J.
Ashby, S.
Bourban, S.
Flynn, M.
Guillemot, M.
Hain, T.
Kadlec, J.
Karaiskos, V.
Kraaij, W.
Kronenthal, M.
Lathoud, G.
Lincoln, M.
Lisowska, A.
McCowan, L.
Post, W.
Reidsma, D.
Wellner, P.
Corporatie AMI Project Consortium
TNO Defensie en Veiligheid
Publication year
2006
Abstract
Abstract. The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-o' to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated.
Subject
Informatics
Data acquisition
Information technology
Learning systems
Microphones
Multi agent systems
Project management
Set theory
Video cameras
Web browsers
AMI Meeting
Multi-modal data set
Orthographic transcription
Recordings
Technical presentations
To reference this document use:
http://resolver.tudelft.nl/uuid:e6d04197-2afd-45b8-ac84-c3d327c91b27
TNO identifier
15884
ISBN
9783540325499
ISSN
0302-9743
Source
2nd International Workshop on Machine Learning for Multimodal Interaction, MLMI 2005, 11 July 2005 through 13 July 2005, Edinburgh., 28-39
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document type
conference paper