Medet Mukushev


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Using Computer Vision to Analyze Non-manual Marking of Questions in KRSL
Anna Kuznetsova | Alfarabi Imashev | Medet Mukushev | Anara Sandygulova | Vadim Kimmelman
Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)

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. The primary focus of the study is to demonstrate the utility of computer vision solutions for the linguistic analysis of non-manuals in sign languages, although additional corrections are required to account for biases in the output. To this end, we analyzed recordings of 10 triplets of sentences produced by 9 native signers using both manual annotation and computer vision solutions (such as OpenFace). We utilize and improve the computer vision solution, and briefly describe the results of the linguistic analysis.


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Evaluation of Manual and Non-manual Components for Sign Language Recognition
Medet Mukushev | Arman Sabyrov | Alfarabi Imashev | Kenessary Koishybay | Vadim Kimmelman | Anara Sandygulova
Proceedings of the 12th Language Resources and Evaluation Conference

The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign’s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2% of accuracy for dataset with 20 signs and 77.9% of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website:

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A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL
Alfarabi Imashev | Medet Mukushev | Vadim Kimmelman | Anara Sandygulova
Proceedings of the 24th Conference on Computational Natural Language Learning

The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics. To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem. However, signers use other articulators: facial expressions, head and body position and movement to convey linguistic information. Given the important role of non-manual markers, this paper proposes a dataset and presents a use case to stress the importance of including non-manual features to improve the recognition accuracy of signs. To the best of our knowledge no prior publicly available dataset exists that explicitly focuses on non-manual components responsible for the grammar of sign languages. To this end, the proposed dataset contains 28250 videos of signs of high resolution and quality, with annotation of manual and non-manual components. We conducted a series of evaluations in order to investigate whether non-manual components would improve signs’ recognition accuracy. We release the dataset to encourage SLR researchers and help advance current progress in this area toward real-time sign language interpretation. Our dataset will be made publicly available at

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Automatic Classification of Handshapes in Russian Sign Language
Medet Mukushev | Alfarabi Imashev | Vadim Kimmelman | Anara Sandygulova
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives

Handshapes are one of the basic parameters of signs, and any phonological or phonetic analysis of a sign language must account for handshapes. Many sign languages have been carefully analysed by sign language linguists to create handshape inventories. This has theoretical implications, but also applied use, as it is important due to the need of generating corpora for sign languages that can be searched, filtered, sorted by different sign components (such as handshapes, orientation, location, movement, etc.). However, it is a very time-consuming process, thus only a handful of sign languages have such inventories. This work proposes a process of automatically generating such inventories for sign languages by applying automatic hand detection, cropping, and clustering techniques. We applied our proposed method to a commonly used resource: the Spreadthesign online dictionary (, in particular to Russian Sign Language (RSL). We then manually verified the data to be able to perform classification. Thus, the proposed pipeline can serve as an alternative approach to manual annotation, and can help linguists in answering numerous research questions in relation to handshape frequencies in sign languages.