@inproceedings{sevilla:20023:sign-lang:lrec, author = {Sevilla, Antonio F. G. and D{\'i}az Esteban, Alberto and Lahoz-Bengoechea, Jos{\'e} Mar{\'i}a}, title = {Tools for the use of {SignWriting} as a Language Resource}, pages = {203--208}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna}, booktitle = {Proceedings of the {LREC2020} 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives}, maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {16}, month = may, year = {2020}, isbn = {979-10-95546-54-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20023.html}, abstract = {Representation of linguistic data is an issue of utmost importance when developing language resources, but the lack of a standard written form in sign languages presents a challenge. Different notation systems exist, but only SignWriting seems to have some use in the native signer community. It is, however, a difficult system to use computationally, not based on a linear sequence of characters. We present the project "VisSE", which aims to develop tools for the effective use of SignWriting in the computer. The first of these is an application which uses computer vision to interpret SignWriting, understanding the meaning of new or existing transcriptions, or even hand-written images. Two additional tools will be able to consume the result of this recognizer: first, a textual description of the features of the transcription will make it understandable for non-signers. Second, a three-dimensional avatar will be able to reproduce the configurations and movements contained within the transcription, making it understandable for signers even if not familiar with SignWriting. Additionally, the project will result in a corpus of annotated SignWriting data which will also be of use to the computational linguistics community.} } @inproceedings{sevilla-lahozbengoechea-2026-multiband:lrec, author = {Sevilla, Antonio F. G. and Lahoz-Bengoechea, Jos{\'e} Mar{\'i}a}, title = {Decomposing Sign Language Movements: A Multi-Band Visualization Method for Articulatory Analysis}, pages = {9559--9568}, editor = {Piperidis, Stelios and Bel, N{\'u}ria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio}, booktitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)}, publisher = {{ELRA Language Resources Association (ELRA)}}, address = {Palma, Mallorca, Spain}, day = {11--16}, month = may, year = {2026}, isbn = {978-2-493814-49-4}, language = {english}, url = {https://lrec.elra.info/lrec2026-main-751}, doi = {10.63317/32sdurbs4fio}, abstract = {Understanding the structure of sign language movements requires methods that can isolate and analyze the hierarchical and simultaneous nature of sign articulation. We present a method for tracking and visualizing sign language movements that progressively isolates dependent movements within the articulatory chain: hand rotation from arm displacement and finger movement from hand movement. Using MediaPipe hand tracking on ordinary 2D video, we decompose motion into separate gestural components and compute velocity and direction for each articulator. We present these movement channels in a time-aligned multi-band visualization that reveals temporal structure, bimanual synchronization patterns, and the coordination of different articulatory components. An interactive web-based viewer synchronizes the visualization with video, enabling researchers to efficiently explore movement patterns and their relationship to signing. We demonstrate the method with examples from isolated signs and continuous signing, showing how it reveals patterns that are difficult to observe in raw video, including bimanual coordination, internal movements, and the distinction between linguistic and non-linguistic segments. This approach provides accessible tools for empirical investigation of rhythmic and prosodic patterns in sign languages.} } @inproceedings{sevilla-etal-2024-prosodic:lrec, author = {Sevilla, Antonio F. G. and Lahoz-Bengoechea, Jos{\'e} Mar{\'i}a and D{\'i}az Esteban, Alberto}, title = {Automated Extraction of Prosodic Structure from Unannotated Sign Language Video}, pages = {1808--1816}, editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}, booktitle = {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 = {20--25}, month = may, year = {2024}, isbn = {978-2-493814-10-4}, language = {english}, url = {https://aclanthology.org/2024.lrec-main.161}, abstract = {As in oral phonology, prosody is an important carrier of linguistic information in sign languages. One of the most prominent ways this reveals itself is in the time structure of signs: their rhythm and intensity of articulation. To be able to empirically see these effects, the velocity of the hands can be computed throughout the execution of a sign. In this article, we propose a method for extracting this information from unlabeled videos of sign language, exploiting CoTracker, a recent advancement in computer vision which can track every point in a video without the need of any calibration or fine-tuning. The dominant hand is identified via clustering of the computed point velocities, and its dynamic profile plotted to make apparent the prosodic structure of signing. We apply our method to different datasets and sign languages, and perform a preliminary visual exploration of results. This exploration supports the usefulness of our methodology for linguistic analysis, though issues to be tackled remain, such as bi-manual signs and a formal and numerical evaluation of accuracy. Nonetheless, the absence of any preprocessing requirements may make it useful for other researchers and datasets.} }