@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.}
}