CONF gatica03a-conf/IDIAP On automatic annotation of meeting databases Gatica-Perez, Daniel McCowan, Iain A. Barnard, Mark Bengio, Samy Bourlard, Hervé EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-06.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/gatica03a Related documents IEEE International Conference on Image Processing (ICIP) 2003 In this paper, we discuss meetings as an application domain for multimedia content analysis. Meeting databases are a rich data source suitable for a variety of audio, visual and multi-modal tasks, including speech recognition, people and action recognition, and information retrieval. We specifically focus on the task of semantic annotation of audio-visual (AV) events, where annotation consists of assigning labels (event names) to the data. In order to develop an automatic annotation system in a principled manner, it is essential to have a well-defined task, a standard corpus and an objective performance measure. In this work we address each of these issues to automatically annotate events based on participant interactions. REPORT gatica03a/IDIAP On automatic annotation of meeting databases Gatica-Perez, Daniel McCowan, Iain A. Barnard, Mark Bengio, Samy Bourlard, Hervé EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-06.pdf PUBLIC Idiap-RR-06-2003 2003 IDIAP In this paper, we discuss meetings as an application domain for multimedia content analysis. Meeting databases are a rich data source suitable for a variety of audio, visual and multi-modal tasks, including speech recognition, people and action recognition, and information retrieval. We specifically focus on the task of semantic annotation of audio-visual (AV) events, where annotation consists of assigning labels (event names) to the data. In order to develop an automatic annotation system in a principled manner, it is essential to have a well-defined task, a standard corpus and an objective performance measure. In this work we address each of these issues to automatically annotate events based on participant interactions.