Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers

TL;DR

This paper introduces a simple, training-free system that effectively associates faces with voices in videos by fusing audio and visual signals, achieving around 71% accuracy without prior environmental knowledge.

Contribution

The work presents a novel, data-agnostic approach to speaker-face association in videos by combining weak speech clustering with visual cues, suitable for web-based videos.

Findings

Achieved approximately 71% accuracy in real-world tests.

Does not require task-specific training data.

Effective in environments lacking prior spatial or environmental information.

Abstract

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.