• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.17-24
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• 2011

In recent, the enhancement of guide signs system for expressway were studied. And newly suggested signs in that research show left-right bilingual (Korean/English) configuration of 2 destinations whereas existing signs showed top-bottom configuration. However the new configuration have not been evaluated with discreet examination. In this study, several bilingual configuration designs are developed and evaluated with human factors experiment. Therefore the design with the shortest legibility time were chosen as a best design. As a result, the fact that the change of Korean/English configuration shows different results of the legibility time was confirmed. The guide sign with left-right bilingual (Korean/English) configuration of 2 destinations showed less legibility time than that of the existing signs with top-bottom configuration.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1535-1545
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• 2022

Sign language has completely different meaning depending on the direction of the hand or the change of facial expression even with the same gesture. In this respect, it is crucial to capture the spatial-temporal structure information of each movement. However, sign language translation studies based on Sign2Gloss2Text only convey comprehensive spatial-temporal information about the entire sign language movement. Consequently, detailed information (facial expression, gestures, and etc.) of each movement that is important for sign language translation is not emphasized. Accordingly, in this paper, we propose Spatial-temporal Keypoints Centered Sign2Gloss2Text Translation, named STKC-Sign2 Gloss2Text, to supplement the sequential and semantic information of keypoints which are the core of recognizing and translating sign language. STKC-Sign2Gloss2Text consists of two steps, Spatial Keypoints Embedding, which extracts 121 major keypoints from each image, and Temporal Keypoints Embedding, which emphasizes sequential information using Bi-GRU for extracted keypoints of sign language. The proposed model outperformed all Bilingual Evaluation Understudy(BLEU) scores in Development(DEV) and Testing(TEST) than Sign2Gloss2Text as the baseline, and in particular, it proved the effectiveness of the proposed methodology by achieving 23.19, an improvement of 1.87 based on TEST BLEU-4.

• Smart Media Journal
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• v.12 no.1
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.137-142
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• 1997

One of the inherent problems in constructing the sign language dictionary is how to make retrieval and comparison operations on the visual database of signs. This paper describes a retrieval method, especifically for Japanese signs. This method has a useful capability for flexible retrieval of the sign from a bilingual dictionary. Our method can retrieve similar signs to the given input. The retrieval mechanism is essentially based on similarity between the given verbal description and verbal descriptions in a retrieval database. The similarity measure of verbal descriptions can be considered as the approximations for the similarity of sign motion images. As a results of our experiment, the success ratio of the retrievals is 96% in averages.

• Journal of KIISE
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• v.44 no.2
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• pp.163-170
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• 2017

Despite the rise of studies in spoken to sign language translation, low-resource problems of sign language corpus have been rarely addressed. As a first step towards translating from spoken to sign language, we addressed the problems arising from resource scarcity when translating spoken language to manual signals translation using statistical machine translation techniques. More specifically, we proposed three preprocessing methods: 1) paraphrase generation, which increases the size of the corpora, 2) lemmatization, which increases the frequency of each word in the corpora and the translatability of new input words in spoken language, and 3) elimination of function words that are not glossed into manual signals, which match the corresponding constituents of the bilingual sentence pairs. In our experiments, we used different types of English-American sign language parallel corpora. The experimental results showed that the system with each method and the combination of the methods improved the quality of manual signals translation, regardless of the type of the corpora.