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 [BibTeX] [Marc21]
Detecting Group Interest-level in Meetings
Type of publication: Conference paper
Citation: gatica05a-conf
Booktitle: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP)
Year: 2005
Crossref: gatica-rr-04-51:
Abstract: Finding relevant segments in meeting recordings is important for summarization, browsing, and retrieval purposes. In this paper, we define relevance as the interest-level that meeting participants manifest as a group during the course of their interaction (as perceived by an external observer,',','), and investigate the automatic detection of segments of high-interest from audio-visual cues. This is motivated by the assumption that there is a relationship between segments of interest to participants, and those of interest to the end user, e.g. of a meeting browser. We first address the problem of human annotation of group interest-level. On a 50-meeting corpus, recorded in a room equipped with multiple cameras and microphones, we found that the annotations generated by multiple people exhibit a good degree of consistency, providing a stable ground-truth for automatic methods. For the automatic detection of high-interest segments, we investigate a methodology based on Hidden Markov Models (HMMs) and a number of audio and visual features. Single- and multi-stream approaches were studied. Using precision and recall as performance measures, the results suggest that the automatic detection of group interest-level is promising, and that while audio in general constitutes the predominant modality in meetings, the use of a multi-modal approach is beneficial.
Userfields: ipdmembership={speech, learning, vision},
Keywords:
Projects Idiap
Authors Gatica-Perez, Daniel
McCowan, Iain A.
Zhang, Dong
Bengio, Samy
Added by: [UNK]
Total mark: 0
Attachments
  • gatica-rr-04-51.pdf
  • gatica-rr-04-51.ps.gz
Notes