  In this paper, we present a system that associates faces with voices in a
video by fusing information from the audio and visual signals. The thesis
underlying our work is that an extremely simple approach to generating (weak)
speech clusters can be combined with visual signals to effectively associate
faces and voices by aggregating statistics across a video. This approach does
not need any training data specific to this task and leverages the natural
coherence of information in the audio and visual streams. It is particularly
applicable to tracking speakers in videos on the web where a priori information
about the environment (e.g., number of speakers, spatial signals for
beamforming) is not available. We performed experiments on a real-world dataset
using this analysis framework to determine the speaker in a video. Given a
ground truth labeling determined by human rater consensus, our approach had
~71% accuracy.
