@inproceedings{kimmelman:24008:sign-lang:lrec,
  author    = {Kimmelman, Vadim and Oomen, Marloes and Pfau, Roland},
  title     = {Headshakes in {NGT}: Relation between Phonetic Properties {\&} Linguistic Functions},
  pages     = {159--167},
  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/24008.html},
  doi       = {10.63317/4eew3sj6ypi4},
  abstract  = {Non-manual markers (such as facial expressions and head movements) have been shown to fulfil a wide range of grammatical functions across sign languages. One nonmanual marker that is very wide-spread is headshake used to express negation. While negation and headshake have been studied for a variety of sign languages, phonetic/kinematic research on headshake has been mostly absent. In this paper, we conduct a phonetic analysis of headshake in Sign Language of the Netherlands using a Computer Vision solution, namely OpenFace. We specifically analyze whether linguistic properties of headshake (e.g. spreading and the type of signs co-occurring with the headshake) influence its phonetic form.}
}

@inproceedings{kimmelman:24009:sign-lang:lrec,
  author    = {Kimmelman, Vadim and Price, Ari and Safar, Josefina and de Vos, Connie and Bulla, Jan},
  title     = {Nonmanual Marking of Questions in {Balinese} Homesign Interactions: a Computer-Vision Assisted Analysis},
  pages     = {168--177},
  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/24009.html},
  doi       = {10.63317/5aur9arxz9vb},
  abstract  = {In recent years, both linguistic resources and computer-based tools have been developed that make it possible to investigate research questions that have not been studied before. In this study, we conduct a study of nonmanual question marking, using data from the Balinese Homesign Corpus -- a unique resource documenting language use in several Balinese homesigners. We further demonstrate how using OpenFace, a Computer-Vision solution, allows for quantitative analysis of head tilts used by these signers in marking questions. We also showcase a pilot statistical analysis of the dynamic kinetic contours of the head movements.}
}

@inproceedings{susman:24005:sign-lang:lrec,
  author    = {Susman, Margaux and Kimmelman, Vadim},
  title     = {Eye Blink Detection in Sign Language Data Using {CNNs} and Rule-Based Methods},
  pages     = {361--369},
  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/24005.html},
  doi       = {10.63317/4oxyyek4f24q},
  abstract  = {Eye blinks are used in a variety of sign languages as prosodic boundary markers. However, no cross-linguistic quantitative research on eye blinks exists. In order to facilitate such research in future, we develop and test different methods of automatic eyeblink identification, based on a linguistic definition of blinks, and in a dataset of a natural sign language (French Sign Language). We compare two main approaches to eye openness detection: calculating the Eye Aspect Ratio using MediaPipe, and training CNNs to detect openness directly based on images from the video recordings. For the CNN method, we train different models (with different numbers of signers in the training data, different frame crops and different numbers of epochs). We then combine the openness degree detection with a separate rule-based component in order to determine boundaries of blink events. We demonstrate that both methods perform relatively well, and discuss the practical implications of the methods.}
}