no code implementations • LREC 2014 • Rosalee Wolfe, John McDonald, Larwan Berke, Marie Stumbo
Corpus analysis is a powerful tool for signed language synthesis.
no code implementations • LREC 2020 • Ronan Johnson, Rosalee Wolfe
Of the five phonemic parameters in sign language (handshape, location, palm orientation, movement and nonmanual expressions), the one that still poses the most challenges for effective avatar display is nonmanual signals.
no code implementations • MTSummit 2021 • John C. McDonald, Rosalee Wolfe, Eleni Efthimiou, Evita Fontinea, Frankie Picron, Davy Van Landuyt, Tina Sioen, Annelies Braffort, Michael Filhol, Sarah Ebling, Thomas Hanke, Verena Krausneker
Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process.
no code implementations • SLTAT (LREC) 2022 • Athanasia-Lida Dimou, Vassilis Papavassiliou, John McDonald, Theodore Goulas, Kyriaki Vasilaki, Anna Vacalopoulou, Stavroula-Evita Fotinea, Eleni Efthimiou, Rosalee Wolfe
One major goal of the project is the direct involvement of sign language (SL) users at every stage of development of the project’s signing avatar.
no code implementations • SLTAT (LREC) 2022 • John McDonald, Ronan Johnson, Rosalee Wolfe
An avatar that produces legible, easy-to-understand signing is one of the essential components to an effective automatic signed/spoken translation system.
no code implementations • SLTAT (LREC) 2022 • Rosalee Wolfe, John McDonald, Ronan Johnson, Ben Sturr, Syd Klinghoffer, Anthony Bonzani, Andrew Alexander, Nicole Barnekow
This paper describes efforts to improve avatar mouthing by addressing these challenges, resulting in a new approach for mouthing animation.