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

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.

References in corpus (1)

• YouTube-8M: A Large-Scale Video Classification Benchmark

Cited by in corpus (10)

• Deep Audio-Visual Learning: A Survey
• Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging
• Attention-based Residual Speech Portrait Model for Speech to Face Generation
• Self-supervised learning for audio-visual speaker diarization
• Reconstructing faces from voices
• Audio-visual Speaker Recognition with a Cross-modal Discriminative Network
• Look Who's Talking: Active Speaker Detection in the Wild
• HLT-NUS Submission for NIST 2019 Multimedia Speaker Recognition Evaluation
• Binaural Audio Generation via Multi-task Learning
• CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language