WSLP-2026 hosted three shared tasks on Indian Sign Language: sentence-level translation from video or pose into English (SLT), isolated recognition over a large one-shot vocabulary (ISLR), and word/sign presence prediction in continuous sentence video (WPP). Shared tasks were hosted on Codabench. Twenty-three teams registered for translation and five submitted; thirteen registered for WPP and five submitted; twenty-two registered for recognition and nine submitted. On the lexical tracks, the systems that report usable official scores treat the problem as retrieval or temporal alignment within a pose-embedding space, often with an external exemplar bank. Translation submissions almost uniformly map pose sequences into a pretrained T5 decoder. They obtain fluent English more readily than scores that can be trusted at the current accuracy level: official BLEU can sit at zero, while chrF remains small and sensitive to output length. The edition shows that ISL can be measured by automatic evaluation metrics, but that current scores still say more about retrieval recipes and decoder fluency than about signed grammar. A later edition should mark pose-tracker failures and place native-signer judgments alongside BLEU and chrF.

We organized the Second Workshop on Sign Language Processing (WSLP-2026), co-located with EMNLP 2026 in Budapest, Hungary, on 29 October 2026. The workshop continues the programme begun at WSLP 2025: to give under-resourced sign languages a dedicated computational venue, and to treat sign language processing as a set of related problems rather than a single translation benchmark. We received 40 submissions and accepted 22 papers. This overview concentrates on the 15 accepted main-workshop research papers. Those papers cover new corpora for Catalan, Uruguayan, Icelandic, Russian, and Bangla sign languages; compact gloss-free translation; isolated recognition and fingerspelling; and a cluster of diagnostic studies on what actually transfers when a model is moved from a high-resource sign language to a low-resource one. As part of the workshop, we hosted three shared tasks on Indian Sign Language, namely translation, isolated recognition, and word/sign presence prediction, which together attracted 58 registered teams, and 19 teams actively participated. The open questions have shifted from whether sign languages can be modeled at all to how annotation budgets should be allocated, which parts of a pretrained checkpoint are worth transferring, and how to evaluate systems when automatic metrics remain only weakly correlated with grammatical correctness.

This paper discusses the design, recording and preprocessing of a Czech sign language corpus. The corpus is intended for training and testing of sign language recognition (SLR) systems. The UWB-07-SLR-P corpus contains video data of 4 signers recorded from 3 different perspectives. Two of the perspectives contain whole body and provide 3D motion data, the third one is focused on signer’s face and provide data for face expression and lip feature extraction. Each signer performed 378 signs with 5 repetitions. The corpus consists of several types of signs: numbers (35 signs), one and two-handed finger alphabet (64), town names (35) and other signs (244). Each sign is stored in a separate AVI file. In total the corpus consists of 21853 video files in total length of 11.1 hours. Additionally each sign is preprocessed and basic features such as 3D hand and head trajectories are available. The corpus is mainly focused on feature extraction and isolated SLR rather than continuous SLR experiments.

In this paper we discuss the design, acquisition and preprocessing of a Czech audio-visual speech corpus. The corpus is intended for training and testing of existing audio-visual speech recognition system. The name of the database is UWB-07-ICAVR, where ICAVR stands for Impaired Condition Audio Visual speech Recognition. The corpus consists of 10,000 utterances of continuous speech obtained from 50 speakers. The total length of the database is 25 hours. Each utterance is stored as a separate sentence. The corpus extends existing databases by covering condition of variable illumination. We acquired 50 speakers, where half of them were men and half of them were women. Recording was done by two cameras and two microphones. Database introduced in this paper can be used for testing of visual parameterization in audio-visual speech recognition (AVSR). Corpus can be easily split into training and testing part. Each speaker pronounced 200 sentences: first 50 were the same for all, the rest of them were different. Six types of illumination were covered. Session for one speaker can fit on one DVD disk. All files are accompanied by visual labels. Labels specify region of interest (mouth and area around them specified by bounding box). Actual pronunciation of each sentence is transcribed into the text file.