| Paper ID | IVMSP-27.6 |
| Paper Title |
SEEHEAR: SIGNER DIARISATION AND A NEW DATASET |
| Authors |
Samuel Albanie, University of Oxford, United Kingdom; Gül Varol, Ecole des Ponts, Univ Gustave Eiffel, France; Liliane Momeni, Triantafyllos Afouras, Andrew Brown, Chuhan Zhang, Ernesto Coto, University of Oxford, France; Necati Cihan Camgöz, Ben Saunders, University of Surrey, United Kingdom; Abhishek Dutta, University of Oxford, United Kingdom; Neil Fox, University College London, United Kingdom; Richard Bowden, University of Surrey, United Kingdom; Bencie Woll, University College London, United Kingdom; Andrew Zisserman, University of Oxford, United Kingdom |
| Session | IVMSP-27: Multi-modal Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this work, we propose a framework to collect a large-scale, diverse sign language dataset that can be used to train automatic sign language recognition models. The first contribution of this work is SDTRACK, a generic method for signer tracking and diarisation in the wild. Our second contribution is SeeHear, a dataset of 90 hours of British Sign Language (BSL) content featuring more than1000 signers, and including interviews, monologues and debates. Using SDTRACK, the SeeHear dataset is annotated with 35K active signing tracks, with corresponding signer identities and subtitles, and 40K automatically localised sign labels. As a third contribution, we provide benchmarks for signer diarisation and sign recognition on SEEHEAR. |
