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Browsing Bergen Open Research Archive by Author "Imashev, Alfarabi"

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    • Automatic Classification of Handshapes in Russian Sign Language 

      Mukushev, Medet; Imashev, Alfarabi; Kimmelman, Vadim; Sandygulova, Anara (Chapter, 2020)
      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 ...
    • Evaluation of Manual and Non-manual Components for Sign Language Recognition 

      Mukushev, Medet; Sabyrov, Arman; Imashev, Alfarabi; Koishibay, Kenessary; Kimmelman, Vadim; Sandygulova, Anara (Chapter, 2020)
      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 ...
    • Eyebrow position in grammatical and emotional expressions in Kazakh-Russian Sign Language: A quantitative study 

      Kimmelman, Vadim; Imashev, Alfarabi; Mukushev, Medet; Sandygulova, Anara (Journal article; Peer reviewed, 2020-06)
      Facial expressions in sign languages are used to express grammatical functions, such as question marking, but can also be used to express emotions (either the signer’s own or in constructed action contexts). Emotions and ...
    • Functional Data Analysis of Non-manual Marking of Questions in Kazakh-Russian Sign Language 

      Kuznetsova, Anna; Imashev, Alfarabi; Mukushev, Medet; Sandygulova, Anara; Kimmelman, Vadim (Chapter, 2022)
      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 ...
    • K-RSL: a Corpus for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages 

      Imashev, Alfarabi; Mukushev, Medet; Kimmelman, Vadim; Sandygulova, Anara (Chapter, 2020)
      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 ...

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