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This archive contains the RWTH-Weather-Phoenix 2014 signer independent SI5 continuous sign language recognition corpus.
It is released under non-commercial cc 4.0 license with attribution (see attachment)
If you use this data in your research, please cite:
O. Koller, J. Forster, and H. Ney. Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers. Computer Vision and Image Understanding, volume 141, pages 108-125, December 2015.
(for the general corpus)
and
Koller, Zargaran, Ney. "Re-Sign: Re-Aligned End-to-End Sequence Modeling with Deep Recurrent CNN-HMMs" in CVPR 2017, Honululu, Hawaii, USA.
(for the signer independent setup)
The signer independent SI5 partition contains 8 signers in train and 1 signer (unseen signer, which is signer 5) in test and has been recorded on the broadcastnews channel.
phoenix-2014-signerindependent-SI5
├── annotations
│ │
│ └── manual -> this contains the corpus files
│
├── evaluation -> this contains an evaluation script. make sure to have a compiled version of the NIST sclite tools in your path. call: ./evaluatePhoenix2014.sh example-hypothesis-dev.ctm dev
│
├── features
│ │
│ └── fullFrame-210x260px -> resolution of 210x260 pixels, but they are distorted due to transmission channel particularities, to undistort stretch images to 210x300
│ ├── dev
│ ├── test
│ └── train
│
│
└── models -> we provide caffe models to achieve the published 45.1 / 44.1 % WER on the dev/test partition of this corpus, we also provide our languagemodel
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