Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers
This addresses speaker tracking in web videos where prior environmental information is unavailable, representing an incremental improvement.
The paper tackles the problem of associating faces with voices in videos by fusing audio and visual signals without task-specific training data, achieving about 71% accuracy on a real-world dataset.
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.