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, 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 • 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.
1 code implementation • SignLang (LREC) 2022 • Anastasia Chizhikova, Vadim Kimmelman
We applied OpenFace, a Computer Vision toolkit, to extract head rotation measurements from video recordings, and analyzed the headshake in terms of the number of peaks (turns), the amplitude of the turns, and their frequency.
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 • 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 • 15 Mar 2024 • Anna Kuznetsova, Vadim Kimmelman
Advances in Deep Learning have made possible reliable landmark tracking of human bodies and faces that can be used for a variety of 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.
no code implementations • LREC 2020 • Medet Mukushev, Alfarabi Imashev, Vadim Kimmelman, S, Anara ygulova
However, it is a very time-consuming process, thus only a handful of sign languages have such inventories.
no code implementations • LREC 2020 • Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, S, Anara ygulova
The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign.