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dc.contributor.authorMukushev, Medet
dc.contributor.authorImashev, Alfarabi
dc.contributor.authorKimmelman, Vadim
dc.contributor.authorSandygulova, Anara
dc.description.abstractHandshapes 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 an inventory is necessary for generating corpora for sign languages that can be searched, filtered, sorted by different sign components (such as handshapes, orientation, location, movement, etc.). However, creating an inventory is a very time-consuming process, thus only a handful of sign languages have them. Therefore, in this work we firstly test an unsupervised approach with the aim to automatically generate a handshape inventory. The process includes hand detection, cropping, and clustering techniques, which we apply to a commonly used resource: the Spreadthesign online dictionary (, in particular to Russian Sign Language (RSL). We then manually verify the data to be able to apply supervised learning to classify new data.en_US
dc.publisherEuropean Language Resources Association (ELRA)en_US
dc.relation.ispartofProceedings of the LREC 2020 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
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.titleAutomatic Classification of Handshapes in Russian Sign Languageen_US
dc.rights.holderCopyright European Language Resources Association (ELRA)en_US
dc.identifier.citationProceedings of the 9th Workshop on the Representation and Processing of Sign Languages. 2020, 165–170en_US

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Navngivelse-Ikkekommersiell 4.0 Internasjonal
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