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Found 11 papers, 2 papers with code
Using Computer Vision to Analyze Non-manual Marking of Questions in KRSL
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.
Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50
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.
Phonetics of Negative Headshake in Russian Sign Language: A Small-Scale Corpus Study
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.
Functional Data Analysis of Non-manual Marking of Questions in Kazakh-Russian Sign Language
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.
Towards Large Vocabulary Kazakh-Russian Sign Language Dataset: KRSL-OnlineSchool
To this end, this corpus contains video recordings of Kazakhstan’s online school translated to Kazakh-Russian sign language by 7 interpreters.
Towards Semi-automatic Sign Language Annotation Tool: SLAN-tool
The SLAN-tool provides a web-based service for the annotation of sign language videos.
Testing MediaPipe Holistic for Linguistic Analysis of Nonmanual Markers in Sign Languages
Advances in Deep Learning have made possible reliable landmark tracking of human bodies and faces that can be used for a variety of tasks.
A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL
To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem.
Evaluation of Manual and Non-manual Components for Sign Language Recognition
The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign.
Automatic Classification of Handshapes in Russian Sign Language
However, it is a very time-consuming process, thus only a handful of sign languages have such inventories.
