Automatic detection of group functional roles in face to face interactions
Proceedings of the 8th international conference on Multimodal interfaces
Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information
IEEE Transactions on Computers
Using the influence model to recognize functional roles in meetings
Proceedings of the 9th international conference on Multimodal interfaces
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
An information theoretic approach to speaker diarization of meeting data
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Multimedia
An overview of automatic speaker diarization systems
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
Several recent works on social signals have addressed the problem of statistical modeling of social interaction in multi-party discussions showing that characteristics like turn-taking patterns can be modeled and predicted according to the role that each participant has in the discussion. Reversely this work investigates the use of social signals to improve conventional speech processing methods. In details we propose the use of turn-taking patterns induced by roles for improving speaker diarization, the task of determining 'Who spoke when' in an audio file. In detail, this work studies how to include this information as statistical prior on the speaker interactions for segmenting and clustering speakers in multi-party political debates. Experiments reveal that the proposed approach reduces the speaker error over the baseline by 25% when both the number of speakers and their roles are known and by 13% relative when the pattern information is estimated from the data. Furthermore we never verify a performance degradation in any recording. Experiments are also carried out to investigate the contribution of the first-order Markov assumption i.e. that the role of the speaker n is conditionally dependent on the role of the speaker n-1.