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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
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