Papers by Anara Sandygulova
Evaluation of Manual and Non-manual Components for Sign Language Recognition (2020.lrec-1)
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Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, Anara Sandygulova
| Challenge: | Deaf communities communicate via sign languages to express meaning and intent. |
| Approach: | They used sign samples from 20 commonly used signs in Kazakh-Russian Sign Language (K-RSL) to investigate whether non-manual components would improve sign’s recognition accuracy. |
| Outcome: | The results showed that using non-manual components would improve sign recognition accuracy. |
Comparative Analysis of Sign Language Interpreting Agents Perception: A Study of the Deaf (2024.lrec-main)
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| Challenge: | Prior research on sign language recognition has shown encouraging outcomes in achieving highly accurate and dependable automatic sign language generation. |
| Approach: | They propose to compare a state-of-the-art sign language generation system with a skilled sign language interpreter to gain insights into usability of such metrics for deaf signers. |
| Outcome: | The proposed system is compared with a skilled interpreter to gain insights into usability of such metrics for deaf signers and how deafic signers perceive signing agents. |
Cyrillic-MNIST: a Cyrillic Version of the MNIST Dataset (2022.lrec-1)
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| Challenge: | Existing datasets for computer vision to distinguish printed or handwritten characters in digital images are limited to one language. |
| Approach: | They propose to use a Cyrillic version of the MNIST dataset to analyze handwritten letters. |
| Outcome: | The proposed dataset is compared to the Extended MNIST (EMNIST) dataset and is available at https://github.com/bolattleubayev/cmnist. |
Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50 (2022.lrec-1)
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| Challenge: | Using crowdsourcing, we created a signer independent dataset for sign language processing. |
| Approach: | They propose to crowdsource a signer independent Kazakh-Russian Sign Language (KRSL) dataset. |
| Outcome: | The proposed dataset consists of 173 sentences performed by 50 signers for 43,250 video samples. |