{"url":"/dataset/rwth-phoenix-weather-2014-t","name":"RWTH-PHOENIX-Weather 2014 T","full_name":null,"description_markdown":"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.\r\n\r\nThe 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.","description_withheld":null,"homepage":"https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/","introduced_date":"2018-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/neural-sign-language-translation","title":"Neural Sign Language Translation","first_author":"Necati Cihan Camgoz","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Sign Language Recognition","url":"/task/sign-language-recognition","datasets_with_task":"/datasets/task/sign-language-recognition"},{"name":"Sign Language Translation","url":"/task/sign-language-translation","datasets_with_task":"/datasets/task/sign-language-translation"},{"name":"Gloss-free Sign Language Translation","url":"/task/gloss-free-sign-language-translation","datasets_with_task":"/datasets/task/gloss-free-sign-language-translation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["RWTH-PHOENIX-Weather 2014 T","PHOENIX14T"],"data_loaders":[],"num_papers_in_archive":114,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sign-language-recognition-on-rwth-phoenix-1","task":"Sign Language Recognition","dataset_variant":"RWTH-PHOENIX-Weather 2014 T","rows":15,"metrics":["Word Error Rate (WER)"],"first_row_in_archive_order":{"model":"SlowFastSign","paper":"/paper/slowfast-network-for-continuous-sign-language","metrics":{"Word Error Rate (WER)":"18.7"},"code_links":[{"title":"kaistmm/SlowFastSign","url":"https://github.com/kaistmm/SlowFastSign"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/sign-language-translation-on-rwth-phoenix","task":"Sign Language Translation","dataset_variant":"RWTH-PHOENIX-Weather 2014 T","rows":11,"metrics":["BLEU-4","ROUGE"],"first_row_in_archive_order":{"model":"MSKA-SLT","paper":"/paper/multi-stream-keypoint-attention-network-for","metrics":{"BLEU-4":"29.03"},"code_links":[{"title":"sutwangyan/MSKA","url":"https://github.com/sutwangyan/MSKA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/clip-sla-parameter-efficient-clip-adaptation","title":"CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition","date":"2025-04-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/swin-mstp-swin-transformer-with-multi-scale","title":"Swin-MSTP: Swin transformer with multi-scale temporal perception for continuous sign language recognition","date":"2025-02-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/signformer-is-all-you-need-towards-edge-ai-1","title":"Signformer is all you need: Towards Edge AI for Sign Language","date":"2024-11-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-stream-keypoint-attention-network-for","title":"Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation","date":"2024-05-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/tcnet-continuous-sign-language-recognition","title":"TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions","date":"2024-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/slowfast-network-for-continuous-sign-language","title":"SlowFast Network for Continuous Sign Language Recognition","date":"2023-09-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multimodal-locally-enhanced-transformer-for","title":"Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition","date":"2023-08-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/signbert-hand-model-aware-self-supervised-pre","title":"SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding","date":"2023-05-08","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/two-stream-network-for-sign-language","title":"Two-Stream Network for Sign Language Recognition and Translation","date":"2022-11-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/a-simple-multi-modality-transfer-learning","title":"A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation","date":"2022-03-08","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/c2slr-consistency-enhanced-continuous-sign","title":"C2SLR: Consistency-Enhanced Continuous Sign Language Recognition","date":"2022-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stochastic-transformer-networks-with-linear","title":"Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation","date":"2021-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/frozen-pretrained-transformers-for-neural","title":"Frozen Pretrained Transformers for Neural Sign Language Translation","date":"2021-08-20","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/improving-sign-language-translation-with","title":"Improving Sign Language Translation with Monolingual Data by Sign Back-Translation","date":"2021-05-26","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/self-mutual-distillation-learning-for","title":"Self-Mutual Distillation Learning for Continuous Sign Language Recognition","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stochastic-fine-grained-labeling-of-multi","title":"Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition","date":"2020-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/continuous-sign-language-recognition-through","title":"Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space","date":"2020-05-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sign-language-translation-with-transformers","title":"Better Sign Language Translation with STMC-Transformer","date":"2020-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spatial-temporal-multi-cue-network-for","title":"Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition","date":"2020-02-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/neural-sign-language-translation","title":"Neural Sign Language Translation","date":"2018-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":27,"samples_ran":17,"samples_unverified":10,"pointer_only_for_licence":21,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}