@inproceedings{caligiore-etal-2024-multisource,
title = "Multisource Approaches to {I}talian {S}ign {L}anguage ({LIS}) Recognition: Insights from the {M}ulti{M}eda{LIS} Dataset",
author = "Caligiore, Gaia and
Mineo, Raffaele and
Spampinato, Concetto and
Ragonese, Egidio and
Palazzo, Simone and
Fontana, Sabina",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.17/",
pages = "132--140",
ISBN = "979-12-210-7060-6",
abstract = "Given their status as unwritten visual-gestural languages, research on the automatic recognition of sign languages has increasingly implemented multisource capturing tools for data collection and processing. This paper explores advancements in Italian Sign Language (LIS) recognition using a multimodal dataset in the medical domain: the MultiMedaLIS Dataset. We investigate the integration of RGB frames, depth data, optical flow, and skeletal information to develop and evaluate two computational models: Skeleton-Based Graph Convolutional Network (SL-GCN) and Spatiotemporal Separable Convolutional Network (SSTCN). RADAR data was collected but not included in the testing phase. Our experiments validate the effectiveness of these models in enhancing the accuracy and robustness of isolated LIS signs recognition. Our findings highlight the potential of multisource approaches in computational linguistics to improve linguistic accessibility and inclusivity for members of the signing community."
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<abstract>Given their status as unwritten visual-gestural languages, research on the automatic recognition of sign languages has increasingly implemented multisource capturing tools for data collection and processing. This paper explores advancements in Italian Sign Language (LIS) recognition using a multimodal dataset in the medical domain: the MultiMedaLIS Dataset. We investigate the integration of RGB frames, depth data, optical flow, and skeletal information to develop and evaluate two computational models: Skeleton-Based Graph Convolutional Network (SL-GCN) and Spatiotemporal Separable Convolutional Network (SSTCN). RADAR data was collected but not included in the testing phase. Our experiments validate the effectiveness of these models in enhancing the accuracy and robustness of isolated LIS signs recognition. Our findings highlight the potential of multisource approaches in computational linguistics to improve linguistic accessibility and inclusivity for members of the signing community.</abstract>
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%0 Conference Proceedings
%T Multisource Approaches to Italian Sign Language (LIS) Recognition: Insights from the MultiMedaLIS Dataset
%A Caligiore, Gaia
%A Mineo, Raffaele
%A Spampinato, Concetto
%A Ragonese, Egidio
%A Palazzo, Simone
%A Fontana, Sabina
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F caligiore-etal-2024-multisource
%X Given their status as unwritten visual-gestural languages, research on the automatic recognition of sign languages has increasingly implemented multisource capturing tools for data collection and processing. This paper explores advancements in Italian Sign Language (LIS) recognition using a multimodal dataset in the medical domain: the MultiMedaLIS Dataset. We investigate the integration of RGB frames, depth data, optical flow, and skeletal information to develop and evaluate two computational models: Skeleton-Based Graph Convolutional Network (SL-GCN) and Spatiotemporal Separable Convolutional Network (SSTCN). RADAR data was collected but not included in the testing phase. Our experiments validate the effectiveness of these models in enhancing the accuracy and robustness of isolated LIS signs recognition. Our findings highlight the potential of multisource approaches in computational linguistics to improve linguistic accessibility and inclusivity for members of the signing community.
%U https://aclanthology.org/2024.clicit-1.17/
%P 132-140
Markdown (Informal)
[Multisource Approaches to Italian Sign Language (LIS) Recognition: Insights from the MultiMedaLIS Dataset](https://aclanthology.org/2024.clicit-1.17/) (Caligiore et al., CLiC-it 2024)
ACL