@inproceedings{martinod:22014:sign-lang:lrec, author = {Martinod, Emmanuella and Danet, Claire and Filhol, Michael}, title = {Two New {AZee} Production Rules Refining Multiplicity in {French} {Sign} {Language}}, pages = {132--138}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {25}, month = jun, year = {2022}, isbn = {979-10-95546-86-3}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22014.html}, abstract = {This paper is a contribution to sign language (SL) modeling. We focus on the hitherto imprecise notion of "Multiplicity", assumed to express plurality in French Sign Language (LSF), using AZee approach. AZee is a linguistic and formal approach to modeling LSF. It takes into account the linguistic properties and specificities of LSF while respecting constraints linked to a modeling process. We present the methodology to extract AZee production rules. Based on the analysis of strong form-meaning associations in SL data (elicited image descriptions and short news), we identified two production rules structuring the expression of multiplicity in LSF. We explain how these newly extracted production rules are different from existing ones. Our goal is to refine the AZee approach to allow the coverage of a growing part of LSF. This work could lead to an improvement in SL synthesis and SL automatic translation.} } @inproceedings{bigand:70004:sltat:lrec, author = {Bigand, F{\'e}lix and Prigent, Elise and Braffort, Annelies}, title = {Synthesis for the Kinematic Control of Identity in Sign Language}, pages = {1--6}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee}, booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {24}, month = jun, year = {2022}, isbn = {979-10-95546-82-5}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/2022.sltat-1.1.html}, abstract = {Sign Language (SL) animations generated from motion capture (mocap) of real signers convey critical information about their identity. It has been suggested that this information is mostly carried by statistics of the movements kinematics. Manipulating these statistics in the generation of SL movements could allow controlling the identity of the signer, notably to preserve anonymity. This paper tests this hypothesis by presenting a novel synthesis algorithm that manipulates the identity-specific statistics of mocap recordings. The algorithm produced convincing new versions of French Sign Language discourses, which accurately modulated the identity prediction of a machine learning model. These results open up promising perspectives toward the automatic control of identity in the motion animation of virtual signers.} } @inproceedings{choudhury:70011:sltat:lrec, author = {Choudhury, Shatabdi}, title = {Analysis of Torso Movement for Signing Avatar Using Deep Learning}, pages = {7--12}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee}, booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {24}, month = jun, year = {2022}, isbn = {979-10-95546-82-5}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/2022.sltat-1.2.html}, abstract = {Avatars are virtual or on-screen representations of a human used in various roles for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language with believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human signing. Such details are generally not included in the linguistic annotation. Nevertheless, these motions are highly essential to displaying lifelike and communicative animations. This paper presents a deep learning model for use in a signing avatar. The study focuses on coordinating torso movements and other human body parts. The proposed model will automatically compute the torso rotation based on the avatar's wrist positions. The resulting motion can improve the user experience and engagement with the avatar.} } @inproceedings{belissen-etal-2020-dicta:lrec, author = {Belissen, Valentin and Braffort, Annelies and Gouiff{\`e}s, Mich{\`e}le}, title = {{D}icta-{S}ign-{LSF}-v2: Remake of a Continuous {F}rench {S}ign {L}anguage Dialogue Corpus and a First Baseline for Automatic Sign Language Processing}, pages = {6040--6048}, editor = {Calzolari, Nicoletta and Fr{\'e}d{\'e}ric B{\'e}chet 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 Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios}, booktitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {11--16}, month = may, year = {2020}, isbn = {979-10-95546-34-4}, language = {english}, url = {https://aclanthology.org/2020.lrec-1.740}, abstract = {While the research in automatic Sign Language Processing (SLP) is growing, it has been almost exclusively focused on recognizing lexical signs, whether isolated or within continuous SL production. However, Sign Languages include many other gestural units like iconic structures, which need to be recognized in order to go towards a true SL understanding. In this paper, we propose a newer version of the publicly available SL corpus Dicta-Sign, limited to its French Sign Language part. Involving 16 different signers, this dialogue corpus was produced with very few constraints on the style and content. It includes lexical and non-lexical annotations over 11 hours of video recording, with 35000 manual units. With the aim of stimulating research in SL understanding, we also provide a baseline for the recognition of lexical signs and non-lexical structures on this corpus. A very compact modeling of a signer is built and a Convolutional-Recurrent Neural Network is trained and tested on Dicta-Sign-LSF-v2, with state-of-the-art results, including the ability to detect iconicity in SL production.} } @inproceedings{malala:18028:sign-lang:lrec, author = {Malala, Vonjiniaina Domohina and Prigent, Elise and Braffort, Annelies and Berret, Bastien}, title = {Which Picture? A Methodology for the Evaluation of Sign Language Animation Understandability}, pages = {115--120}, 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/18028.html}, abstract = {The goal of our study is to explore which information is essential to understand virtual signing. To that aim, we developed an online test to assess the comprehensibility of four different versions of signers: a baseline version with a real human signer, a most complete version of a virtual signer, and two degraded versions of a virtual signer (one with non-visible hands and one without movements of head/trunk). Each video showed the description of a picture in French Sign Language (LSF). After having seen the video, participants had to find which picture had been described among 9 pictures displayed. The originality of our approach was to include two types of confusable pictures on the response board. One was supposed to induce errors by confounding the lexical signs and the other by confounding the spatial structure of the picture. In this way, we explored the effect of hiding hands and blocking trunk/head on the comprehension of lexicon and spatial structure.} } @inproceedings{filhol-hadjadj-2018-elicitation:lrec, author = {Filhol, Michael and Hadjadj, Mohamed Nassime}, title = {Elicitation protocol and material for a corpus of long prepared monologues in Sign Language}, pages = {4241--4246}, editor = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu}, booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Miyazaki, Japan}, day = {7--12}, month = may, year = {2018}, isbn = {979-10-95546-00-9}, language = {english}, url = {https://aclanthology.org/L18-1669}, abstract = {In this paper, we address collection of prepared Sign Language discourse, as opposed to spontaneous signing. Specifically, we aim at collecting long discourse, which creates problems explained in the paper. Being oral and visual languages, they cannot easily be produced while reading notes without distorting the data, and eliciting long discourse without influencing the production order is not trivial. For the moment, corpora contain either short productions, data distortion or disfluencies. We propose a protocol and two tasks with their elicitation material to allow cleaner long-discourse data, and evaluate the result of a recent test with LSF informants.} } @inproceedings{benchiheub:16029:sign-lang:lrec, author = {Benchiheub, Mohamed-El-Fatah and Berret, Bastien and Braffort, Annelies}, title = {Collecting and Analysing a Motion-Capture Corpus of {French} {Sign} {Language}}, pages = {7--12}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna}, booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining}, maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Portoro{\v z}, Slovenia}, day = {28}, month = may, year = {2016}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16029.html}, abstract = {This paper presents a 3D corpus of motion capture data on French Sign Language (LSF), which is the first one available for the scientific community for pluridisciplinary studies. The paper also exhibits the usefulness of performing kinematic analysis on the corpus. The goal of the analysis is to acquire informative and quantitative knowledge for the purpose of better understanding and modelling LSF movements. Several LSF native signers are involved in the project. They were asked to describe 25 pictures in a spontaneous way while the 3D position of various body parts was recorded. Data processing includes identifying the markers, interpolating the information of missing frames, and importing the data to an annotation software to segment and classify the signs. Finally, we present the results of an analysis performed to characterize information-bearing parameters and use them in a data mining and modelling perspective.} }