Paper Info
Title
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 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 VVAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets inthe-wild - WildV-VAD - 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.
Year
DOI
Venue
2020
10.1109/ICPR48806.2021.9412884
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
DocType
ISSN
Citations 
Conference
1051-4651
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Sylvain Guy100.34
Stéphane Lathuilière2335.98
Pablo Mesejo3163.01
Radu Horaud42776261.99