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SEEHEAR: signer diarisation and a new dataset

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 than 1000 signers, and including interviews, monologues and debates. Using SDTrack, the SeeHear dataset is an...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICASSP39728.2021.9414856

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-1732-9198
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/R03298X/1
Publisher:
IEEE
Journal:
Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) More from this journal
Issue:
2021
Pages:
2280-2284
Publication date:
2021-05-13
Acceptance date:
2021-01-30
Event title:
2021 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021)
Event location:
Toronto, ON, Canada
Event website:
https://2021.ieeeicassp.org/
Event start date:
2021-06-06
Event end date:
2021-06-11
DOI:
EISSN:
2379-190X
ISSN:
1520-6149
EISBN:
978-1-7281-7605-5
ISBN:
978-1-7281-7606-2
Language:
English
Keywords:
Pubs id:
1169226
Local pid:
pubs:1169226
Deposit date:
2021-03-24

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