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Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Abstract : 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 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, based on combining A-VAD and face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with such a dataset.
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Submitted on : Wednesday, September 23, 2020 - 11:39:19 AM
Last modification on : Tuesday, September 21, 2021 - 2:16:03 PM


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  • HAL Id : hal-02882229, version 2



Sylvain Guy, Stéphane Lathuilière, Pablo Mesejo, Radu Horaud. Learning Visual Voice Activity Detection with an Automatically Annotated Dataset. International Conference on Pattern Recognition, Jan 2021, Milano, Italy. ⟨hal-02882229v2⟩



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