Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
The Rules Behind Roles: Identifying Speaker Role in Radio Broadcasts
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Automatic detection of group functional roles in face to face interactions
Proceedings of the 8th international conference on Multimodal interfaces
IEEE Transactions on Multimedia
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Automatic nonverbal analysis of social interaction in small groups: A review
Image and Vision Computing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Using linguistic and vocal expressiveness in social role recognition
Proceedings of the 16th international conference on Intelligent user interfaces
Inferring competitive role patterns in reality TV show through nonverbal analysis
Multimedia Tools and Applications
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This paper presents an approach for the recognition of roles in multiparty recordings. The approach includes two major stages: extraction of Social Affiliation Networks (speaker diarization and representation of people in terms of their social interactions), and role recognition (application of discrete probability distributions to map people into roles). The experiments are performed over several corpora, including broadcast data and meeting recordings, for a total of roughly 90 hours of material. The results are satisfactory for the broadcast data (around 80 percent of the data time correctly labeled in terms of role), while they still must be improved in the case of the meeting recordings (around 45 percent of the data time correctly labeled). In both cases, the approach outperforms significantly chance.