1 code implementation • SignLang (LREC) 2022 • Anna Kuznetsova, Alfarabi Imashev, Medet Mukushev, Anara Sandygulova, Vadim Kimmelman
This paper is a continuation of Kuznetsova et al. (2021), which described non-manual markers of polar and wh-questions in comparison with statements in an NLP dataset of Kazakh-Russian Sign Language (KRSL) using Computer Vision.
no code implementations • SignLang (LREC) 2022 • Medet Mukushev, Aigerim Kydyrbekova, Vadim Kimmelman, Anara Sandygulova
To this end, this corpus contains video recordings of Kazakhstan’s online school translated to Kazakh-Russian sign language by 7 interpreters.
no code implementations • SignLang (LREC) 2022 • Medet Mukushev, Arman Sabyrov, Madina Sultanova, Vadim Kimmelman, Anara Sandygulova
The SLAN-tool provides a web-based service for the annotation of sign language videos.
no code implementations • LREC 2022 • Medet Mukushev, Aigerim Kydyrbekova, Alfarabi Imashev, Vadim Kimmelman, Anara Sandygulova
This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing.
no code implementations • LREC 2022 • Bolat Tleubayev, Zhanel Zhexenova, Kenessary Koishybay, Anara Sandygulova
This paper presents a new handwritten dataset, Cyrillic-MNIST, a Cyrillic version of the MNIST dataset, comprising of 121, 234 samples of 42 Cyrillic letters.
no code implementations • MTSummit 2021 • Anna Kuznetsova, Alfarabi Imashev, Medet Mukushev, Anara Sandygulova, Vadim Kimmelman
This paper presents a study that compares non-manual markers of polar and wh-questions to statements in Kazakh-Russian Sign Language (KRSL) in a dataset collected for NLP tasks.
no code implementations • CONLL 2020 • Alfarabi Imashev, Medet Mukushev, Vadim Kimmelman, Anara Sandygulova
To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem.