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Visual voice activity detection (V-VAD) uses visual features to predictwhether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD)is inefficient either because the acoustic signal is difficult to analyze orbecause it is simply missing. We propose two deep architectures for V-VAD, onebased on facial landmarks and one based on optical flow. Moreover, availabledatasets, used for learning and for testing V-VAD, lack content variability. Weintroduce a novel methodology to automatically create and annotate very largedatasets in-the-wild -- WildVVAD -- based on combining A-VAD with facedetection and tracking. A thorough empirical evaluation shows the advantage oftraining the proposed deep V-VAD models with this dataset.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset