Estimating Dominance in Multi-Party Meetings Using Speaker Diarization

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.