@inproceedings{sharma:24041:sign-lang:lrec, author = {Sharma, Paritosh and Challant, Camille and Filhol, Michael}, title = {Facial Expressions for Sign Language Synthesis using {FACSHuman} and {AZee}}, pages = {354--360}, 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/24041.html}, abstract = {This paper presents an approach to synthesising facial expressions on signing avatars. We implement those generated by a recently proposed set of rules formalised in the AZee framework for LSF. Our methodology combines computer vision, linguistic insights, and morph target animation to address the challenges posed by the synthesis of nuanced facial expressions, which are pivotal for conveying emotions and grammatical cues in sign language. By implementing a set of universally applicable morphs and incorporating these advancements into our animation system, we aim to improve the realism and expressiveness of signing avatars. Our findings suggest an enhancement in the synthesis of non-manual signals, which extends to multiple avatars. This work opens new avenues for future research, including the exploration of more sophisticated facial modelling techniques and the potential integration of facial motion capture data to refine the animation of facial expressions further.} } @inproceedings{challant-filhol-2024-nonmanual:lrec, author = {Challant, Camille and Filhol, Michael}, title = {Extending {AZee} with Non-manual Gesture Rules for {French} {Sign} {Language}}, pages = {7007--7016}, 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.614}, abstract = {This paper presents a study on non-manual gestures, using a formal model named AZee. This is an approach which allows to formally represent Sign Language (SL) discourses, but also to animate them with a virtual signer. As non-manual gestures are essential in SL and therefore necessary for a quality synthesis, we wanted to extend AZee with them, by adding some production rules to the AZee production set. For this purpose, we applied a methodology which allows to find new production rules on a corpus representing one hour of French Sign Language, the 40 br{\`e}ves (Challant and Filhol, 2022). 23 production rules for non-manual gestures in LSF have thus been determined. We took advantage of this study to directly insert these new rules in the first corpus of AZee discourses expressions, which describe with AZee the productions in SL of the 40 br{\`e}ves corpus. 533 non-manual rules were inserted in the corpus, and some updates were made. This article proposes a new version of this AZee expressions corpus.} } @inproceedings{challant-filhol-2022-corpus:lrec, author = {Challant, Camille and Filhol, Michael}, title = {A First Corpus of {AZee} Discourse Expressions}, pages = {1560--1565}, editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios}, booktitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {20--25}, month = jun, year = {2022}, isbn = {979-10-95546-72-6}, language = {english}, url = {https://aclanthology.org/2022.lrec-1.167}, abstract = {This paper presents a corpus of AZee discourse expressions, i.e. expressions which formally describe Sign Language utterances of any length using the AZee approach and language. The construction of this corpus had two main goals: a first reference corpus for AZee, and a test of its coverage on a significant sample of real-life utterances. We worked on productions from an existing corpus, namely the "40 breves", containing an hour of French Sign Language. We wrote the corresponding AZee discourse expressions for the entire video content, i.e. expressions capturing the forms produced by the signers and their associated meaning by combining known production rules, a basic building block for these expressions. These are made available as a version 2 extension of the "40 breves". We explain the way in which these expressions can be built, present the resulting corpus and set of production rules used, and perform first measurements on it. We also propose an evaluation of our corpus: for one hour of discourse, AZee allows to describe 94{\%} of it, while ongoing studies are increasing this coverage. This corpus offers a lot of future prospects, for instance concerning synthesis with virtual signers, machine translation or formal grammars for Sign Language.} } @inproceedings{bertinlemee-etal-2022-rosettalsf:lrec, author = {Bertin-Lem{\'e}e, Elise and Braffort, Annelies and Challant, Camille and Danet, Claire and Dauriac, Boris and Filhol, Michael and Martinod, Emmanuella and Segouat, J{\'e}r{\'e}mie}, title = {{Rosetta-LSF}: an Aligned Corpus of {French} {Sign} {Language} and {French} for Text-to-Sign Translation}, pages = {4955--4962}, editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios}, booktitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {20--25}, month = jun, year = {2022}, isbn = {979-10-95546-72-6}, language = {english}, url = {https://aclanthology.org/2022.lrec-1.529}, abstract = {This article presents a new French Sign Language (LSF) corpus called "Rosetta-LSF". It was created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer. An overview of the field highlights the importance of a quality representation of LSF. In order to obtain quality animations understandable by signers, it must surpass the simple "gloss transcription" of the LSF lexical units to use in the discourse. To achieve this, we designed a corpus composed of four types of aligned data, and evaluated its usability. These are: news headlines in French, translations of these headlines into LSF in the form of videos showing animations of a virtual signer, gloss annotations of the "traditional" type---although including additional information on the context in which each gestural unit is performed as well as their potential for adaptation to another context---and AZee representations of the videos, i.e. formal expressions capturing the necessary and sufficient linguistic information. This article describes this data, exhibiting an example from the corpus. It is available online for public research.} }