RWTH-PHOENIX-Weather 2014 T: Parallel Corpus of Sign Language Video, Gloss and Translation


Human Language Technology & Pattern Recognition Group

RWTH Aachen University, Germany


In Short:

you can download here the RWTH-PHOENIX Weather 2014 T (39GB) Continuous sign language recognition and translation benchmark data set. If you use it in your research, please cite:

Necati Cihan Camgöz, Simon Hadfield, Oscar Koller, Hermann Ney, Richard Bowden, Neural Sign Language Translation, IEEE Conf. on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018.

Detailed Description:

Over a period of three years (2009 - 2011) the daily news and weather forecast airings of the German public tv-station PHOENIX featuring sign language interpretation have been recorded and the weather forecasts of a subset of 386 editions have been transcribed using gloss notation. Furthermore, we used automatic speech recognition with manual cleaning to transcribe the original German speech. As such, this corpus allows to train end-to-end sign language translation systems from sign language video input to spoken language.

The signing is recorded by a stationary color camera placed in front of the sign language interpreters. Interpreters wear dark clothes in front of an artificial grey background with color transition. All recorded videos are at 25 frames per second and the size of the frames is 210 by 260 pixels. Each frame shows the interpreter box only.


RWTH-PHOENIX Weather 2014 T Contents:
    1. README
        1. README


Published State of the Art Continuous Sign Language Recognition Results on RWTH-PHOENIX-Weather 2014 Multisigner:
Author BLEU-4 Dev BLEU-4 Test
Camgoz, Hadfield, Koller Ney and Bowden, CVPR 2018 18.4 18.13


Oscar Koller 2017-11-05
Creative Commons License