@inproceedings{malaia:26027:sign-lang:lrec,
  author    = {Malaia, Evie A. and Krebs, Julia and Harbour, Eric and Martetschl{\"a}ger, Julia and Schwameder, Hermann and Roehm, Dietmar and Wilbur, Ronnie B.},
  title     = {The Displacement-Velocity Dissociation in Sign Language Learning: Kinematic Signatures of Event Structure in Novice {{\"O}GS} Signers},
  pages     = {324--332},
  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/26027.html},
  abstract  = {This study investigates how adult learners acquire linguistically contrastive movement patterns in Austrian Sign Language ({\"O}GS), focusing on the telic/atelic distinction predicted by the Event Visibility Hypothesis. Telic verbs (bounded events) are produced by proficient Deaf signers with shorter duration and temporally precise, low-entropy velocity profiles, whereas atelic verbs (unbounded processes) show more continuous motion. Using 3D motion capture (300 Hz), we compared 8 novice learners (6--12 weeks of instruction) with 6 proficient Deaf signers across 71 verbs. Linear mixed-effects models revealed a dissociation between gross movement patterning and fine-grained velocity profile structure in learner productions. Learners correctly reproduced the proportional path-length contrast between telic and atelic verbs, replicating the gross spatial distinction of proficient signers. However, temporal marking of the telic/atelic contrast was underproduced: learners showed a significantly smaller duration difference between verb types than proficient signers, while total path length did not differ significantly between verb types or groups. Temporal control showed significant between-group differences: learners exhibited elevated sample entropy, with non-proficient velocity profiles within individual sign productions, though spatial consistency across trials (STI) was comparable to that of proficient signers. Peak velocity did not differ between groups, suggesting that learners can reach target speeds but cannot yet modulate temporal structure reliably. These findings support distinct learning trajectories for gross movement patterning and fine-grained motion complexity, and demonstrate that velocity profile structure within signs constitutes a core linguistic target in sign language learning.}
}

@inproceedings{sazonov:26056:sign-lang:lrec,
  author    = {Sazonov, Dmitriy and Gurbuz, Sevgi and Malaia, Evie A.},
  title     = {Lost in Expression: Diagnosing Systemic Challenges with Non-Manual Generalization in Sign Language Understanding Tasks},
  pages     = {438--449},
  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/26056.html},
  abstract  = {Incorporation of non-manual information is one of the most challenging aspects of Sign Language Understanding (SLU), as these features contribute to the semantic, syntactic, and pragmatic structure of signed communication as a critical feature of compositional meaning at sign, phrase and sentence level. Despite their key linguistic role, non-manuals are often an afterthought in SLU model and dataset design, with many recent models still neglecting to implement non-manual analysis or evaluate how articulators beyond the hands are contributing to the model prediction. In this work, we identify and analyze the challenges relating to recognition of non-manuals and generalization of their linguistic roles encountered by SLU models, offering new explanations for failures to properly model non-manual behavior. We perform a case study on the subtasks of Continuous Sign Language Recognition and Sign Language Translation by applying the Uni-Sign model to Isharah-1000, a Saudi Sign Language dataset. Using controlled partitioning and feature attribution, we further analyze model behavior and failure cases. With this work we hope to set the stage for the creation of diagnostic frameworks for generalization of non-manuals.}
}

@inproceedings{malaia:24049:sign-lang:lrec,
  author    = {Malaia, Evie A. and Borneman, Joshua and Gurbuz, Sevgi},
  title     = {Capturing Motion: Using Radar to Build Better Sign Language Corpora},
  pages     = {213--218},
  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/24049.html},
  abstract  = {Sign language conveys information using dynamic visual signal. Proficient signers rely on the skill in processing and predictive motion information during sign language comprehension. Much current work in sign language corpora development relies on video data. However, from the perspective of information transfer in communication, video recordings are limited in capturing spatial and temporal frequencies of sign language signal in sufficient resolution. In contrast, radar can capture 3D motion data at high temporal and spatial resolution, preserving depth articulations lost in 2D video. Radar's recording parameters can also be adapted in real time to optimize temporal resolution for rapid signing motions. Thus, radar recordings provide higher-fidelity corpora for analyzing linguistic features of sign languages and creating smart environments that respond to signed input. Crucially, radar recordings uphold user privacy, only capturing kinematic parameters of communicative signal, as opposed to signer identity. Radar resolution in capturing dynamic data from sign language production, and privacy advantages it provides to users, make it uniquely suited for advancing sign language research through corpora development.}
}

@inproceedings{mcdonald:16001:sign-lang:lrec,
  author    = {McDonald, John C. and Wolfe, Rosalee and Wilbur, Ronnie and Moncrief, Robyn and Malaia, Evie A. and Fujimoto, Sayuri and Baowidan, Souad and Stec, Jessika},
  title     = {A New Tool to Facilitate Prosodic Analysis of Motion Capture Data and a Datadriven Technique for the Improvement of Avatar Motion},
  pages     = {153--158},
  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/16001.html},
  abstract  = {Researchers have been investigating the potential rewards of utilizing motion capture for linguistic analysis, but have encountered challenges when processing it. A significant problem is the nature of the data: along with the signal produced by the signer, it also contains noise. The first part of this paper is an exposition on the origins of noise and its relationship to motion capture data of signed utterances. The second part presents a tool, based on established mathematical principles, for removing or isolating noise to facilitate prosodic analysis. This tool yields surprising insights into a data-driven strategy for a parsimonious model of life-like appearance in a sparse key-frame avatar.}
}

@inproceedings{krebs-etal-2024-motion:lrec,
  author    = {Krebs, Julia and Malaia, Evie A. and Fessl, Isabella and Wiesinger, Hans-Peter and Roehm, Dietmar and Wilbur, Ronnie and Schwameder, Hermann},
  title     = {Motion Capture Analysis of Verb and Adjective Types in {Austrian} {Sign} {Language} ({{\"O}GS})},
  pages     = {11619--11624},
  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.1015},
  abstract  = {Across a number of sign languages, temporal and spatial characteristics of dominant hand articulation are used to express semantic and grammatical features. In this study of Austrian Sign Language ({\"O}sterreichische Geb{\"a}rdensprache, or {\"O}GS), motion capture data of four Deaf signers is used to quantitatively characterize the kinematic parameters of sign production in verbs and adjectives. We investigate (1) the difference in production between verbs involving a natural endpoint (telic verbs; e.g. arrive) and verbs lacking an endpoint (atelic verbs; e.g. analyze), and (2) adjective signs in intensified vs. non-intensified (plain) forms. Motion capture data analysis using linear-mixed effects models (LME) indicates that both the endpoint marking in verbs, as well as marking of intensification in adjectives, are expressed by movement modulation in {\"O}GS. While the semantic distinction between verb types (telic/atelic) is marked by higher peak velocity and shorter duration for telic signs compared to atelic ones, the grammatical distinction (intensification) in adjectives is expressed by longer duration for intensified compared to non-intensified adjectives. The observed individual differences of signers might be interpreted as personal signing style.}
}