{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T04:02:25Z","timestamp":1748059345410,"version":"3.41.0"},"publisher-location":"Cham","reference-count":59,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031925900","type":"print"},{"value":"9783031925917","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-92591-7_1","type":"book-chapter","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T07:23:42Z","timestamp":1747985022000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling the\u00a0Distribution of\u00a0Human Motion for\u00a0Sign Language Assessment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5383-7202","authenticated-orcid":false,"given":"Oliver","family":"Cory","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9131-0634","authenticated-orcid":false,"given":"Ozge Mercanoglu","family":"Sincan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8811-1156","authenticated-orcid":false,"given":"Matthew","family":"Vowels","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1696-6921","authenticated-orcid":false,"given":"Alessia","family":"Battisti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1218-2062","authenticated-orcid":false,"given":"Franz","family":"Holzknecht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katja","family":"Tissi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Sidler-Miserez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8713-1163","authenticated-orcid":false,"given":"Tobias","family":"Haug","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6511-5085","authenticated-orcid":false,"given":"Sarah","family":"Ebling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3285-8020","authenticated-orcid":false,"given":"Richard","family":"Bowden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"Arendsen, J., Lichtenauer, J.F., Holt, G.T., van Doorn, A.J., Hendriks, E.A.: Acceptability ratings by humans and automatic gesture recognition for variations in sign productions. In: 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp.\u00a01\u20136 (2008). https:\/\/doi.org\/10.1109\/AFGR.2008.4813347","DOI":"10.1109\/AFGR.2008.4813347"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"Bansal, D., et al.: Copycat: Using sign language recognition to help deaf children acquire language skills. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA 2021, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3411763.3451523","DOI":"10.1145\/3411763.3451523"},{"issue":"518","key":"1_CR3","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","volume":"112","author":"DM Blei","year":"2017","unstructured":"Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859\u2013877 (2017). https:\/\/doi.org\/10.1080\/01621459.2017.1285773","journal-title":"J. Am. Stat. Assoc."},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"Camgoz, N.C., Hadfield, S., Koller, O., Ney, H., Bowden, R.: Neural sign language translation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7784\u20137793 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00812","DOI":"10.1109\/CVPR.2018.00812"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Camgoz, N.C., Koller, O., Hadfield, S., Bowden, R.: Sign language transformers: Joint end-to-end sign language recognition and translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. https:\/\/doi.org\/10.48550\/arXiv.2003.13830","DOI":"10.48550\/arXiv.2003.13830"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Cui, R., Liu, H., Zhang, C.: Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1610\u20131618 (2017). https:\/\/api.semanticscholar.org\/CorpusID:7779968","DOI":"10.1109\/CVPR.2017.175"},{"key":"1_CR7","unstructured":"Duolingo: Duolingo - the world\u2019s best way to learn a language (2024). https:\/\/www.duolingo.com, Accessed 13 June 2024"},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Fang, S., Sui, C., Zhang, X., Tian, Y.: Signdiff: learning diffusion models for American sign language production. arXiv preprint arXiv:2308.16082 (2023). https:\/\/doi.org\/10.48550\/arXiv.2308.16082","DOI":"10.48550\/arXiv.2308.16082"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"Feng, X., Lu, X., Si, X.: Taijiquan auxiliary training and scoring based on motion capture technology and dtw algorithm. Int. J. Ambient Comput. Intell. 14, 1\u201315 (2023). https:\/\/doi.org\/10.4018\/IJACI.330539","DOI":"10.4018\/IJACI.330539"},{"key":"1_CR10","doi-asserted-by":"publisher","unstructured":"Gardner, J.R., Pleiss, G., Bindel, D., Weinberger, K.Q., Wilson, A.G.: Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration. In: Advances in Neural Information Processing Systems (2018). https:\/\/doi.org\/10.48550\/arXiv.1809.11165","DOI":"10.48550\/arXiv.1809.11165"},{"key":"1_CR11","doi-asserted-by":"publisher","unstructured":"Gong, J., Foo, L.G., He, Y., Rahmani, H., Liu, J.: Llms are good sign language translators. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18362\u201318372, June 2024. https:\/\/doi.org\/10.48550\/arXiv.2404.00925","DOI":"10.48550\/arXiv.2404.00925"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Grobel, K., Assan, M.: Isolated sign language recognition using hidden markov models. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol.\u00a01, pp. 162\u2013167 vol.1 (1997). https:\/\/doi.org\/10.1109\/ICSMC.1997.625742","DOI":"10.1109\/ICSMC.1997.625742"},{"issue":"3","key":"1_CR13","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1177\/0265532219898382","volume":"37","author":"T Haug","year":"2020","unstructured":"Haug, T., et al.: Validity evidence for a sentence repetition test of swiss german sign language. Lang. Test. 37(3), 412\u2013434 (2020). https:\/\/doi.org\/10.1177\/0265532219898382","journal-title":"Lang. Test."},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Haug, T., Mann, W., Knoch, U. (eds.): The Handbook of Language Assessment Across Modalities. Oxford University Press, Oxford (2022). https:\/\/doi.org\/10.1093\/oso\/9780190885052.001.0001","DOI":"10.1093\/oso\/9780190885052.001.0001"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification (2015). https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"1_CR16","unstructured":"Higgins, I., et al.: beta-VAE: Learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Representations (2017). https:\/\/openreview.net\/forum?id=Sy2fzU9gl"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Holzknecht, F., Haug, T., Battisti, A., Tissi, K., Sidler-Miserez, S., Ebling, S.: Reforming sign language assessment: setting up a longitudinal learner corpus of rated elicited imitation performances to develop an AI-driven sign language assessment system, July 2024. https:\/\/doi.org\/10.13140\/RG.2.2.32214.66886","DOI":"10.13140\/RG.2.2.32214.66886"},{"key":"1_CR18","doi-asserted-by":"publisher","unstructured":"Holzknecht, F., et al.: Automated sign language vocabulary assessment: Comparing human and machine ratings and studying learner perceptions. Language Assessment Quarterly, pp. 1\u201321 (2024). https:\/\/doi.org\/10.1080\/15434303.2024.2364877","DOI":"10.1080\/15434303.2024.2364877"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Ivashechkin, M., Mendez, O., Bowden, R.: Improving 3d pose estimation for sign language. In: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), pp.\u00a01\u20135 (2023). https:\/\/doi.org\/10.1109\/ICASSPW59220.2023.10193629","DOI":"10.1109\/ICASSPW59220.2023.10193629"},{"key":"1_CR20","doi-asserted-by":"publisher","unstructured":"Jain, H., Harit, G.: An unsupervised sequence-to-sequence autoencoder based human action scoring model. In: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp.\u00a01\u20135, November 2019. https:\/\/doi.org\/10.1109\/GlobalSIP45357.2019.8969424","DOI":"10.1109\/GlobalSIP45357.2019.8969424"},{"issue":"6","key":"1_CR21","doi-asserted-by":"publisher","first-page":"2260","DOI":"10.1109\/TCSVT.2020.3017727","volume":"31","author":"H Jain","year":"2021","unstructured":"Jain, H., Harit, G., Sharma, A.: Action quality assessment using siamese network-based deep metric learning. IEEE Trans. Circuits Syst. Video Technol. 31(6), 2260\u20132273 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2020.3017727","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1_CR22","doi-asserted-by":"publisher","unstructured":"Jiang, S., Sun, B., Wang, L., Bai, Y., Li, K., Fu, Y.: Skeleton aware multi-modal sign language recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 3413\u20133423, June 2021. https:\/\/doi.org\/10.48550\/arXiv.2103.08833","DOI":"10.48550\/arXiv.2103.08833"},{"key":"1_CR23","unstructured":"Kim, J.H., Hwang, E.J., Cho, S., Lee, D.H., Park, J.: Sign language production with avatar layering: a critical use case over rare words. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1519\u20131528. European Language Resources Association, Marseille, France, June 2022. https:\/\/aclanthology.org\/2022.lrec-1.163"},{"key":"1_CR24","doi-asserted-by":"publisher","unstructured":"Kindiroglu, A.A., Kara, O., Ozdemir, O., Akarun, L.: Transfer learning for cross-dataset isolated sign language recognition in under-resourced datasets. In: Proceedings of the 18th International Conference on Automatic Face and Gesture Recognition (FG 2024). Institute of Electrical and Electronics Engineers (IEEE) (2024). https:\/\/doi.org\/10.48550\/arXiv.2403.14534","DOI":"10.48550\/arXiv.2403.14534"},{"key":"1_CR25","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017). https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"1_CR26","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2022). https:\/\/doi.org\/10.48550\/arXiv.1312.6114","DOI":"10.48550\/arXiv.1312.6114"},{"key":"1_CR27","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Advances in Neural Information Processing Systems, vol.\u00a028. Curran Associates, Inc. (2015). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2015\/file\/bc7316929fe1545bf0b98d114ee3ecb8-Paper.pdf"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Koller, O., Bowden, R., Ney, H.: Automatic alignment of hamnosys subunits for continuous sign language recognition. In: LREC 2016: 10th edition of the Language Resources and Evaluation Conference, pp. 121 \u2013 128 (2016)","DOI":"10.5244\/C.30.136"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Koller, O., Zargaran, O., Ney, H., Bowden, R.: Deep sign: Hybrid cnn-hmm for continuous sign language recognition. In: The British Machine Vision Conference (BMVC) (2016)","DOI":"10.5244\/C.30.136"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Koller, O., Ney, H., Bowden, R.: Deep hand: how to train a cnn on 1 million hand images when your data is continuous and weakly labelled. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.412"},{"key":"1_CR31","doi-asserted-by":"publisher","unstructured":"Koller, O., Zargaran, S., Ney, H.: Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent cnn-hmms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.364","DOI":"10.1109\/CVPR.2017.364"},{"key":"1_CR32","unstructured":"Krebs, J., et al.: Motion capture analysis of verb and adjective types in Austrian Sign Language (\u00d6GS). In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 11619\u201311624. ELRA and ICCL, Torino, Italia, May 2024. https:\/\/aclanthology.org\/2024.lrec-main.1015"},{"key":"1_CR33","unstructured":"Kusters, A., De\u00a0Meulder, M., O\u2019Brien, D.: Innovations in deaf studies: critically mapping the field. In: Innovations in Deaf Studies: The Role of Deaf Scholars, vol.\u00a012, pp. 1\u201353. Oxford University Press Oxford (2017)"},{"key":"1_CR34","doi-asserted-by":"publisher","unstructured":"Long-fei, C., Nakamura, Y., Kondo, K.: Modeling user behaviors in machine operation tasks for adaptive guidance (2020). https:\/\/doi.org\/10.48550\/arXiv.2003.03025","DOI":"10.48550\/arXiv.2003.03025"},{"key":"1_CR35","doi-asserted-by":"publisher","unstructured":"Lugaresi, C., et al.: Mediapipe: a framework for building perception pipelines (2019). https:\/\/doi.org\/10.48550\/arXiv.1906.08172","DOI":"10.48550\/arXiv.1906.08172"},{"key":"1_CR36","doi-asserted-by":"publisher","unstructured":"Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. https:\/\/doi.org\/10.48550\/arXiv.1903.03295","DOI":"10.48550\/arXiv.1903.03295"},{"issue":"4","key":"1_CR37","first-page":"386","volume":"4","author":"CM Myford","year":"2003","unstructured":"Myford, C.M., Wolfe, E.W.: Detecting and measuring rater effects using many-facet rasch measurement: Part i. J. Appl. Meas. 4(4), 386\u2013422 (2003)","journal-title":"J. Appl. Meas."},{"key":"1_CR38","doi-asserted-by":"publisher","unstructured":"Napier, J., Leeson, L.: Sign language in action. In: Sign Language in Action. RPAL, pp. 50\u201384. Palgrave Macmillan UK, London (2016). https:\/\/doi.org\/10.1057\/9781137309778_3","DOI":"10.1057\/9781137309778_3"},{"key":"1_CR39","doi-asserted-by":"publisher","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, November 2005. https:\/\/doi.org\/10.7551\/mitpress\/3206.001.0001","DOI":"10.7551\/mitpress\/3206.001.0001"},{"issue":"5","key":"1_CR40","doi-asserted-by":"publisher","first-page":"561","DOI":"10.3233\/IDA-2007-11508","volume":"11","author":"S Salvador","year":"2007","unstructured":"Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561\u2013580 (2007). https:\/\/doi.org\/10.3233\/IDA-2007-11508","journal-title":"Intell. Data Anal."},{"issue":"3","key":"1_CR41","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1111\/j.1467-968X.2010.01242.x","volume":"108","author":"W Sandler","year":"2010","unstructured":"Sandler, W.: Prosody and syntax in sign languages. Trans. Philol. Soc. 108(3), 298\u2013328 (2010). https:\/\/doi.org\/10.1111\/j.1467-968X.2010.01242.x","journal-title":"Trans. Philol. Soc."},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Sandler, W., Lillo-Martin, D.C.: Sign Language and Linguistic Universals. Cambridge University Press (2006)","DOI":"10.1017\/CBO9781139163910"},{"key":"1_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/978-3-030-58621-8_40","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Saunders","year":"2020","unstructured":"Saunders, B., Camgoz, N.C., Bowden, R.: Progressive transformers for end-to-end sign language production. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 687\u2013705. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_40"},{"key":"1_CR44","doi-asserted-by":"publisher","unstructured":"Selvaraj, P., Nc, G., Kumar, P., Khapra, M.: OpenHands: Making sign language recognition accessible with pose-based pretrained models across languages. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2114\u20132133. Association for Computational Linguistics, Dublin, Ireland (May 2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.150","DOI":"10.18653\/v1\/2022.acl-long.150"},{"key":"1_CR45","unstructured":"Starner, T., Forbes, S., So, M., Martin, D., Sridhar, R., Deshpande, G., Sepah, S., Shahryar, S., Bhardwaj, K., Kwok, T., Sehgal, D., Hassan, S., Neubauer, B., Vempala, S.A., Tan, A., Heath, J., Kumar, U.U., Mosur, P.V., Hall, T.M., Singh, R., Cui, C.Z., Cameron, G., Dane, S., Tanzer, G.: Popsign ASL v1.0: An isolated american sign language dataset collected via smartphones. In: Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2023), https:\/\/openreview.net\/forum?id=yEf8NSqTPu"},{"issue":"4","key":"1_CR46","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s11263-019-01281-2","volume":"128","author":"S Stoll","year":"2019","unstructured":"Stoll, S., Camgoz, N.C., Hadfield, S., Bowden, R.: Text2Sign: towards sign language production using neural machine translation and generative adversarial networks. Int. J. Comput. Vision 128(4), 891\u2013908 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01281-2","journal-title":"Int. J. Comput. Vision"},{"issue":"4","key":"1_CR47","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/0031-3203(88)90048-9","volume":"21","author":"S Tamura","year":"1988","unstructured":"Tamura, S., Kawasaki, S.: Recognition of sign language motion images. Pattern Recogn. 21(4), 343\u2013353 (1988). https:\/\/doi.org\/10.1016\/0031-3203(88)90048-9","journal-title":"Pattern Recogn."},{"key":"1_CR48","doi-asserted-by":"publisher","unstructured":"Tarigopula, N., Tornay, S., Muralidhar, S., Magimai\u00a0Doss, M.: Towards accessible sign language assessment and learning. In: Proceedings of the 2022 International Conference on Multimodal Interaction, ICMI 2022, pp. 626\u2013631, Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3536221.3556623","DOI":"10.1145\/3536221.3556623"},{"key":"1_CR49","doi-asserted-by":"publisher","unstructured":"Tornay, S., Bowden, R., Doss, M.M., Camg\u00f6z, N.C.: A phonology-based approach for isolated sign production assessment in sign language (20201025 - 20201029). https:\/\/doi.org\/10.1145\/3395035.3425251","DOI":"10.1145\/3395035.3425251"},{"key":"1_CR50","unstructured":"Tornay, S., et al.: Web smile demo: a web application providing automated feedback on sign language vocabulary production. In: 44th Language Testing and Research Colloquium: Language Assessment for a Global, Digital, and More Equitable Era. New York City, USA, 7\u20139 June 2023"},{"issue":"1","key":"1_CR51","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1111\/1473-4192.00024","volume":"12","author":"T Vinther","year":"2002","unstructured":"Vinther, T.: Elicited imitation:a brief overview. Int. J. Appl. Linguist. 12(1), 54\u201373 (2002). https:\/\/doi.org\/10.1111\/1473-4192.00024","journal-title":"Int. J. Appl. Linguist."},{"key":"1_CR52","doi-asserted-by":"publisher","unstructured":"Walsh, H., Ravanshad, A., Rahmani, M., Bowden, R.: A data-driven representation for sign language production. In: Proceedings of the 18th International Conference on Automatic Face and Gesture Recognition (FG 2024). Institute of Electrical and Electronics Engineers (IEEE) (2024). https:\/\/doi.org\/10.48550\/arXiv.2404.11499","DOI":"10.48550\/arXiv.2404.11499"},{"key":"1_CR53","doi-asserted-by":"publisher","unstructured":"Walsh, H., Saunders, B., Bowden, R.: Changing the representation: examining language representation for neural sign language production. In: Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives, pp. 117\u2013124. European Language Resources Association, Marseille, France, June 2022. https:\/\/doi.org\/10.48550\/arXiv.2210.06312","DOI":"10.48550\/arXiv.2210.06312"},{"key":"1_CR54","doi-asserted-by":"publisher","unstructured":"Wen, H., Xu, Y.: Learning to score sign language with two-stage method (2024). https:\/\/doi.org\/10.48550\/arXiv.2404.10383","DOI":"10.48550\/arXiv.2404.10383"},{"key":"1_CR55","doi-asserted-by":"publisher","unstructured":"Wong, R., Camgoz, N.C., Bowden, R.: Sign2GPT: leveraging large language models for gloss-free sign language translation. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.04164","DOI":"10.48550\/arXiv.2405.04164"},{"key":"1_CR56","doi-asserted-by":"publisher","unstructured":"Xu, J., Rao, Y., Yu, X., Chen, G., Zhou, J., Lu, J.: Finediving: a fine-grained dataset for procedure-aware action quality assessment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2949\u20132958, June 2022. https:\/\/doi.org\/10.48550\/arXiv.2204.03646","DOI":"10.48550\/arXiv.2204.03646"},{"issue":"4","key":"1_CR57","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1177\/0265532215594643","volume":"33","author":"X Yan","year":"2016","unstructured":"Yan, X., Maeda, Y., Lv, J., Ginther, A.: Elicited imitation as a measure of second language proficiency: a narrative review and meta-analysis. Lang. Test. 33(4), 497\u2013528 (2016). https:\/\/doi.org\/10.1177\/0265532215594643","journal-title":"Lang. Test."},{"key":"1_CR58","doi-asserted-by":"publisher","unstructured":"Yin, A., Zhong, T., Tang, L., Jin, W., Jin, T., Zhao, Z.: Gloss attention for gloss-free sign language translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2551\u20132562, June 2023. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00251","DOI":"10.1109\/CVPR52729.2023.00251"},{"key":"1_CR59","doi-asserted-by":"publisher","unstructured":"Zhou, B., et al.: Gloss-free sign language translation: improving from visual-language pretraining. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 20871\u201320881, October 2023. https:\/\/doi.org\/10.48550\/arXiv.2307.14768","DOI":"10.48550\/arXiv.2307.14768"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92591-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T07:23:55Z","timestamp":1747985035000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92591-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031925900","9783031925917"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92591-7_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}