Abstract:
We address the challenging Voice Activity Detection (VAD) problem, which determines "Who is Speaking and When?" in audiovisual recordings. The typical audio-based VAD systems can be ineffective in the presence of ambient noise or noise variations. Moreover, due to technical or privacy reasons, audio might not be always available. In such cases, the use of video modality to perform VAD is desirable. Almost all existing visual VAD methods rely on body part detection, e.g., face, lips, or hands. In contrast, we propose a novel visual VAD method operating directly on the entire video frame, without the explicit need of detecting a person or his/her body parts. Our method, named S-VVAD, learns body motion cues associated with speech activity within a weakly supervised segmentation framework. Therefore, it not only detects the speakers/not-speakers but simultaneously localizes the image positions of them. It is an end-to-end pipeline, person-independent and it does not require any prior knowledge nor pre-processing. S-VVAD performs well in various challenging conditions and demonstrates the state-of-the-art results on multiple datasets. Moreover, the better generalization capability of S-VVAD is confirmed for cross-dataset and person-independent scenarios.
