| 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 | |