  Visual voice activity detection (V-VAD) uses visual features to predict
whether 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 or
because it is simply missing. We propose two deep architectures for V-VAD, one
based on facial landmarks and one based on optical flow. Moreover, available
datasets, used for learning and for testing V-VAD, lack content variability. We
introduce a novel methodology to automatically create and annotate very large
datasets in-the-wild -- WildVVAD -- based on combining A-VAD with face
detection and tracking. A thorough empirical evaluation shows the advantage of
training the proposed deep V-VAD models with this dataset.
