@inproceedings{gibet:26046:sign-lang:lrec, author = {Gibet, Sylvie and Reverdy, Cl{\'e}ment and Marteau, Pierre-Fran{\c c}ois}, title = {An Annotation Formalism for a {French--LSF} Bilingual Corpus Supporting Sign Language Generation}, pages = {184--192}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion}, maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)}, publisher = {{ELRA Language Resources Association (ELRA)}}, address = {Palma, Mallorca, Spain}, day = {16}, month = may, year = {2026}, isbn = {978-2-493814-82-1}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26046.html}, doi = {10.63317/3fpfgrum7nxo}, abstract = {This paper introduces an annotation formalism for bilingual corpora of written French and French Sign Language (LSF), based on a manually-produced, expert transcription of LSF video data. The formalism captures the grammatical specificities of LSF, including spatial and iconic mechanisms, while explicitly encoding features that support motor programs for animated signing avatars. We propose a parameterized gloss-based approach, called PGloss-LSF, which integrates syntactic and semantic structures alongside motion features critical for accurate sign synthesis. We illustrate the framework with examples drawn from our bilingual corpus. The annotation process is incremental, ensuring internal consistency and computational tractability through a two-step evaluation: a qualitative assessment aligning generated signs with the annotation language, and a quantitative evaluation via automatic translation using large language models. By bridging the linguistic specificities of sign language with the computational requirements of sign synthesis, this work advances the integration of sign language corpora into multilingual resources and contributes to the standardization of sign language technologies.} } @inproceedings{reverdy:24031:sign-lang:lrec, author = {Reverdy, Cl{\'e}ment and Gibet, Sylvie and Le Naour, Thibaut}, title = {{STK} {LSF}: A Motion Capture Dataset in {LSF} for {SignToKids}}, pages = {315--322}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24031.html}, doi = {10.63317/4y9dcusqas55}, abstract = {This article presents a new bilingual dataset in written French and French Sign Language (LSF), called STK LSF. This corpus is currently being produced as part of the ANR SignToKids project. The aim of this corpus is to provide digital educational tools for deaf children, thereby facilitating the joint learning of LSF and written French. More broadly, it is intended to support future studies on the automatic processing of signed languages. To define this corpus, we focused on several grammatical phenomena typical to LSF, as well as in tales usually studied by hearing children in the second cycle in France. The corpus data represent approximately 1 hour of recording, carried out with a motion capture system (MoCap) offering a spatial precision of less than 1 mm and a temporal precision of 240 Hz. This high level of precision guarantees the quality of the data collected, which will be used both to build pedagogical scenarios in French and LSF, including signing avatar videos, and for automatic translation of text into LSF.} } @inproceedings{naert:18014:sign-lang:lrec, author = {Naert, Lucie and Reverdy, Cl{\'e}ment and Larboulette, Caroline and Gibet, Sylvie}, title = {Per Channel Automatic Annotation of Sign Language Motion Capture Data}, pages = {139--146}, editor = {Bono, Mayumi and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Osugi, Yutaka}, booktitle = {Proceedings of the {LREC2018} 8th Workshop on the Representation and Processing of Sign Languages: Involving the Language Community}, maintitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Miyazaki, Japan}, day = {12}, month = may, year = {2018}, isbn = {979-10-95546-01-6}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/18014.html}, abstract = {Manual annotation is an expensive and time consuming task partly due to the high number of linguistic channels that usually compose sign language data. In this paper, we propose to automatize the annotation of sign language motion capture data by processing each channel separately. Motion features (such as distances between joints or facial descriptors) that take advantage of the 3D nature of motion capture data and the specificity of the channel are computed in order to (i) segment and (ii) label the sign language data. Two methods of automatic annotation of French Sign Language utterances using similar processes are developed. The first one describes the automatic annotation of thirty-two hand configurations while the second method describes the annotation of facial expressions using a closed vocabulary of seven expressions. Results for the two methods are then presented and discussed.} }