@inproceedings{imashev:24023:sign-lang:lrec,
  author    = {Imashev, Alfarabi and Kydyrbekova, Aigerim and Mukushev, Medet and Sandygulova, Anara and Islam, Shynggys and Israilov, Khassan and Makazhanov, Aibek and Yessenbayev, Zhandos},
  title     = {Retrospective of {Kazakh-Russian} {Sign} {Language} Corpus Formation},
  pages     = {111--122},
  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/24023.html},
  abstract  = {Sign language (SL) is a mode of communication that, in most cases, relies on visual perception exclusively and utilizes visual-gestural modality. Sign languages are already universally acknowledged as complete and natural languages. The advent of machine learning techniques has expanded the range of potential applications, not only in industry but also in addressing societal needs. Previous research conducted before 2015 has already demonstrated encouraging outcomes in developing sign language recognition systems that are both quite accurate and resilient. Nevertheless, the effectiveness and utilization of algorithms are impacted not only by their accessibility but also, at times to a greater extent, by the presence of substantial quantities of pertinent data. At the commencement of the local sign language corpus collection in 2015, there was a notable deficit of local Kazakh-Russian sign language (K-RSL) data available for computer vision and machine-learning tasks. There were already corpora of another lexically close Russian Sign Langauge (RSL), but they were aimed at and tailored for research in linguistics. Therefore, we initiated the procedure by collecting pertinent data appropriate for machine-learning purposes. The subsets have been incorporated into the principal corpus and will be subject to future enhancements and refinements. This paper provides a concise overview of the collected components of the Kazakh-Russian Sign Language Corpus and the resulting outcomes derived from them within the last decade.}
}

@inproceedings{imashev-etal-2024-comparative:lrec,
  author    = {Imashev, Alfarabi and Oralbayeva, Nurziya and Baizhanova, Gulmira and Sandygulova, Anara},
  title     = {Comparative Analysis of Sign Language Interpreting Agents Perception: A Study of the Deaf},
  pages     = {3603--3609},
  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.319},
  abstract  = {Prior research on sign language recognition has already demonstrated encouraging outcomes in achieving highly accurate and dependable automatic sign language recognition. The use of virtual characters as virtual assistants has significantly increased in the past decade. However, the progress in sign language generation and output that closely resembles physiologically believable human motions is still in its early stages. This assertion explains the lack of progress in virtual intelligent signing generative systems. Aside from the development of signing systems, scholarly research have revealed a significant deficiency in evaluating sign language generation systems by those who are deaf and use sign language. This paper presents the findings of a user study conducted with deaf signers. The study is aimed at comparing a state-of-the-art sign language generation system with a skilled sign language interpreter. The study focused on testing established metrics to gain insights into usability of such metrics for deaf signers and how deaf signers perceive signing agents.}
}