dc.contributor.author | Guy, Sylvain | |
dc.contributor.author | Lathuilière, Stéphane | |
dc.contributor.author | Mesejo Santiago, Pablo | |
dc.contributor.author | Horaud, Radu | |
dc.date.accessioned | 2021-10-04T07:11:17Z | |
dc.date.available | 2021-10-04T07:11:17Z | |
dc.date.issued | 2020-10-16 | |
dc.identifier.citation | Published version: S. Guy... [et al.]. "Learning Visual Voice Activity Detection with an Automatically Annotated Dataset," 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 4851-4856, doi: [10.1109/ICPR48806.2021.9412884] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/70588 | |
dc.description | This work has been funded by the EU H2020 project #871245 SPRING and by the Multidisciplinary Institute in Artificial Intelligence (MIAI) #ANR-19-P3IA-0003. | es_ES |
dc.description.abstract | Visual voice activity detection (V-VAD) uses visual
features to predict whether a person is speaking or not. VVAD
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 – 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. | es_ES |
dc.description.sponsorship | European Commission 871245 SPRING | es_ES |
dc.description.sponsorship | Multidisciplinary Institute in Artificial Intelligence (MIAI) ANR-19-P3IA-0003 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.title | Learning Visual Voice Activity Detection with an Automatically Annotated Dataset | es_ES |
dc.type | conference output | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/871245 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.type.hasVersion | SMUR | es_ES |