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@ARTICLE{Hung_IEEETRANS.ASL_2010,
         author = {Hung, Hayley and Huang, Yan and Friedland, Gerald and Gatica-Perez, Daniel},
       projects = {Idiap, AMIDA},
          month = {may},
          title = {Estimating Dominance in Multi-Party Meetings Using Speaker Diarization},
        journal = {IEEE Transactions on Audio, Speech, and Language Processing},
         volume = {19},
         number = {4},
           year = {2011},
          pages = {847-860},
       abstract = {With the increase in cheap commercially available sensors,
recording meetings is becoming an increasingly practical option. With
this trend comes the need to summarize the recorded data in semantically
meaningful ways. Here, we investigate the task of automatically measuring
dominance in small group meetings when only a single audio source
is available. Past research has found that speaking length as a single
feature, provides a very good estimate of dominance. For these tasks
we use speaker segmentations generated by our automated faster than
real-time speaker diarization algorithm, where the number of speakers
is not known beforehand. From user-annotated data, we analyze how the
inherent variability of the annotations affects the performance of our
dominance estimation method. We primarily focus on examining of how
the performance of the speaker diarization and our dominance tasks vary
under different experimental conditions and computationally efficient
strategies, and how this would impact on a practical implementation of
such a system. Despite the use of a state-of-the-art speaker diarization
algorithm, speaker segments can be noisy. On conducting experiments
on almost 5 hours of audio-visual meeting data, our results show that
the dominance estimation is robust to increasing diarization noise.},
            pdf = {https://publications.idiap.ch/attachments/papers/2010/Hung_IEEETRANS.ASL_2010.pdf}
}