Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. This raises concerns about whether models truly generalise or instead rely on signer-specific regularities. We conduct signer-fold cross-validation on GFSLT-VLP, GASLT, and SignCL—three leading, publicly available, gloss-free SLT models—on CSL-Daily and PHOENIX14T. Under signer-independent evaluation, performance drops sharply: on PHOENIX14T, GFSLT-VLP falls from BLEU-4 21.44 to 3.59 and ROUGE-L 42.49 to 11.89; GASLT from 15.74 to 8.26; and SignCL from 22.74 to 3.66. We also observe that in CSL-Daily many target sentences are performed by multiple signers, so common splits can place identical sentences in both training and test, inflating absolute scores by rewarding recall of recurring sentences rather than genuine generalisation. These findings indicate that signer-dependent evaluation can substantially overestimate SLT capability. We recommend: (1) adopting signer-independent protocols to ensure generalisation to unseen signers; (2) restructuring datasets to include explicit signer-independent, sentence-disjoint splits for consistent benchmarking; and (3) reporting both signer-dependent and signer-independent results together with train–test sentence overlap to improve transparency and comparability.

We organized the First Workshop on Sign Language Processing (WSLP 2025), co-located with IJCNLP–AACL 2025 at IIT Bombay, to bring together researchers, linguists, and members of the Deaf community and accelerate computational work on under-resourced sign languages.The workshop accepted ten papers—including two official shared-task submissions—that introduced new large-scale resources (a continuous ISL fingerspelling corpus, cross-lingual HamNoSys corpora), advanced multilingual and motion-aware translation models, explored LLM-based augmentation and glossing strategies, and presented lightweight deployable systems for regional languages such as Odia.We ran a three-track shared task on Indian Sign Language that attracted over sixty registered teams and established the first public leaderboards for sentence-level ISL-to-English translation, isolated word recognition, and word-presence prediction.By centring geographic, linguistic, and organiser diversity, releasing open datasets and benchmarks, and explicitly addressing linguistic challenges unique to visual–spatial languages, we significantly broadened the scope of sign-language processing beyond traditionally dominant European and East-Asian datasets, laying a robust foundation for inclusive, equitable, and deployable sign-language AI in the Global South.