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        <title>Handshape-Aware Sign Language Recognition: Extended Datasets and Exploration of Handshape-Inclusive Methods</title>
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        <namePart type="given">Xuan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>The majority of existing work on sign language recognition encodes signed videos without explicitly acknowledging the phonological attributes of signs. Given that handshape is a vital parameter in sign languages, we explore the potential of handshape-aware sign language recognition. We augment the PHOENIX14T dataset with gloss-level handshape labels, resulting in the new PHOENIX14T-HS dataset. Two unique methods are proposed for handshape-inclusive sign language recognition: a single-encoder network and a dual-encoder network, complemented by a training strategy that simultaneously optimizes both the CTC loss and frame-level cross-entropy loss. The proposed methodology consistently outperforms the baseline performance. The dataset and code can be accessed at: www.anonymous.com.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.198</identifier>
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%0 Conference Proceedings
%T Handshape-Aware Sign Language Recognition: Extended Datasets and Exploration of Handshape-Inclusive Methods
%A Zhang, Xuan
%A Duh, Kevin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-duh-2023-handshape
%X The majority of existing work on sign language recognition encodes signed videos without explicitly acknowledging the phonological attributes of signs. Given that handshape is a vital parameter in sign languages, we explore the potential of handshape-aware sign language recognition. We augment the PHOENIX14T dataset with gloss-level handshape labels, resulting in the new PHOENIX14T-HS dataset. Two unique methods are proposed for handshape-inclusive sign language recognition: a single-encoder network and a dual-encoder network, complemented by a training strategy that simultaneously optimizes both the CTC loss and frame-level cross-entropy loss. The proposed methodology consistently outperforms the baseline performance. The dataset and code can be accessed at: www.anonymous.com.
%R 10.18653/v1/2023.findings-emnlp.198
%U https://aclanthology.org/2023.findings-emnlp.198
%U https://doi.org/10.18653/v1/2023.findings-emnlp.198
%P 2993-3002

Markdown (Informal)

[Handshape-Aware Sign Language Recognition: Extended Datasets and Exploration of Handshape-Inclusive Methods](https://aclanthology.org/2023.findings-emnlp.198) (Zhang & Duh, Findings 2023)

• Handshape-Aware Sign Language Recognition: Extended Datasets and Exploration of Handshape-Inclusive Methods (Zhang & Duh, Findings 2023)

ACL

• Xuan Zhang and Kevin Duh. 2023. Handshape-Aware Sign Language Recognition: Extended Datasets and Exploration of Handshape-Inclusive Methods. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2993–3002, Singapore. Association for Computational Linguistics.