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Journal articles on the topic 'Sign Language (SL)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

., Sriramkumar, Pavan Kalayan, Harsha J, and Yashwanth K. "Sign Language Translator." International Research Journal of Computer Science 10, no. 05 (2023): 264–67. http://dx.doi.org/10.26562/irjcs.2023.v1005.28.

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The deaf community in India relies heavily on the Sign Language (SL) as a means of communication. But because so few people are fluent in SL, there is a communication gap between the hearing and deaf communities. The Sign Language Translator (SLT) in this paper employs MediaPipe and LSTM to convert Sign Language (SL) to text and back again. The suggested approach first uses MediaPipe to extract hand movements and facial emotions from movies of SL signals. The LSTM model, which is trained using a sizable dataset of SL signs and their related text labels, is then fed these characteristics. The i

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Subin M. Varghese and K. Aravinthan. "A robust finger detection based sign language recognition using pattern recognition techniques." Scientific Temper 15, spl-1 (2024): 247–53. https://doi.org/10.58414/scientifictemper.2024.15.spl.29.

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Sign language recognition based on finger detection is arguably the main sign language used by most dumb people. It has its own phonetics, grammar and syntax that set it apart from other sign languages. Research related to sign language (SL) is only now becoming standardized. Considering the challenge of recognizing SL, in this work a new method for recognizing SL dynamic gestures is proposed. Sign language (SL) translation systems can be used to help dumb people interact with normal people with the help of a computer. Most studies on continuous recognition of sign language are done by process

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Schwager, Waldemar, and Ulrike Zeshan. "Word classes in sign languages." Parts of Speech: Descriptive tools, theoretical constructs 32, no. 3 (2008): 509–45. http://dx.doi.org/10.1075/sl.32.3.03sch.

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The topic of word classes remains curiously under-represented in the sign language literature due to many theoretical and methodological problems in sign linguistics. This article focuses on language-specific classifications of signs into word classes in two different sign languages: German Sign Language and Kata Kolok, the sign language of a village community in Bali. The article discusses semantic and structural criteria for identifying word classes in the target sign languages. On the basis of a data set of signs, these criteria are systematically tested out as a first step towards an induc

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Qadhi, Omaimah. "Sign language use in healthcare: professionals’ insight." PeerJ 13 (June 2, 2025): e19446. https://doi.org/10.7717/peerj.19446.

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Background and Aim Communication using sign language (SL) between health care providers (HCPs) and deaf and/or hearing-impaired (DHI) patients was reported to be difficult and oftentimes results in a compromised delivery of quality health care to patients. This study surveyed Saudi health care providers on their perception of SL knowledge on the provision of high-quality care to DHI patients. Methods This was a cross-sectional descriptive study among HCPs in different health and primary care centers in Riyadh, Saudi Arabia. The questionnaire was distributed officially by the Department of Surv

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M, Prema, and Gomathi P M. "ANALYSIS ON DIFFERENT METHODS FOR SIGN LANGUAGE IDENTIFICATION." ICTACT Journal on Soft Computing 12, no. 2 (2022): 2567–71. http://dx.doi.org/10.21917/ijsc.2022.0367.

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Sign Language (SL) is the most organized and structured type of hand/arm gesture in the communicative hand/arm gesture taxonomies. The ability of machines to comprehend human actions and meanings has numerous uses. SL identification is one area of focus. SL is employed by the deaf and hard-of-hearing communities to communicate. Hearing-impaired persons communicate via visual indicators instead of vocal communication and sound patterns. SL also uses facial expressions and body postures as a medium of communication. Pattern matching, computer vision, natural language processing are the key facto

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Adamyuk, Natalia. "Contribution of Deaf Researchers of Ukraine to the Development of the National Sign Language." Kadmos 8 (2016): 465–76. https://doi.org/10.32859/kadmos/8/465-476.

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The paper traces the development of the Ukrainian National Sign Language. It highlights the challenges relating to the introduction and application of national sign languages in former Soviet republics, and gives an account of the painstaking activities of the Ukrainian Society of the Deaf and the Sign Language Laboratory. The SL Laboratory, which includes deaf members as well as members belonging to deaf families, widely engages in research, USL teaching and teacher training. It organizes regular nationwide conferences on SL issues and actively collaborates with local, regional and internatio

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Lepp, Lisa, Dimitar Shterionov, Mirella De Sisto, and Grzegorz Chrupała. "Co-Creation for Sign Language Processing and Translation Technology." Information 16, no. 4 (2025): 290. https://doi.org/10.3390/info16040290.

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Sign language machine translation (SLMT)—the task of automatically translating between sign and spoken languages or between sign languages—is a complex task within the field of NLP. Its multi-modal and non-linear nature require the joint efforts of sign language (SL) linguists, technical experts, and SL users. Effective user involvement is a challenge that can be addressed through co-creation. Co-creation has been formally defined in many fields, e.g., business, marketing, educational, and others; however, in NLP and in particular in SLMT, there is no formal, widely accepted definition. Starti

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8

Mertzani, Maria. "SIGN LANGUAGE LITERACY IN THE SIGN LANGUAGE CURRICULUM." Momento - Diálogos em Educação 31, no. 02 (2022): 449–74. http://dx.doi.org/10.14295/momento.v31i02.14504.

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The Sign Language curriculum is a contemporary development which few countries have officially implemented to teach a national standard Sign Language as a first language (L1) and/or mother tongue in the school grades. In these, Sign Language is a mandatory unit, which the deaf child needs to study and develop metalinguistically, as is the case in learning spoken languages as L1. A Sign Language as a metalanguage also means that the curriculum teaches explicit linguistic knowledge for the child to understand gradually how SL functions in different contexts, to make effective choices for meaning

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Naranjo-Zeledón, Luis, Mario Chacón-Rivas, Jesús Peral, and Antonio Ferrández. "Architecture design of a reinforcement environment for learning sign languages." PeerJ Computer Science 7 (October 12, 2021): e740. http://dx.doi.org/10.7717/peerj-cs.740.

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Different fields such as linguistics, teaching, and computing have demonstrated special interest in the study of sign languages (SL). However, the processes of teaching and learning these languages turn complex since it is unusual to find people teaching these languages that are fluent in both SL and the native language of the students. The teachings from deaf individuals become unique. Nonetheless, it is important for the student to lean on supportive mechanisms while being in the process of learning an SL. Bidirectional communication between deaf and hearing people through SL is a hot topic

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Ryumin, D., D. Ivanko, and A. Axyonov. "CROSS-LANGUAGE TRANSFER LEARNING USING VISUAL INFORMATION FOR AUTOMATIC SIGN GESTURE RECOGNITION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 209–16. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-209-2023.

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Abstract. Automatic sign gesture recognition (GR) plays a critical role in facilitating communication between hearing-impaired individuals and the rest of society. However, recognizing sign gestures accurately and efficiently remains a challenging task due to the diversity of sign languages (SLs) and their limited availability of labeled data. This scientific paper proposes a new approach to improving the accuracy of automatic sign GR using cross-language transfer learning with visual information. Two large-scale multimodal SL corpora are utilized as the basic SLs for this study: the Ankara Un

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Hoffmeister, Robert J., Spyridoula Karipi, and Vassilis Kourbetis. "BILINGUAL CURRICULUM MATERIALS SUPPORTING SIGNED LANGUAGE AS A FIRST LANGUAGE FOR DEAF STUDENTS." Momento - Diálogos em Educação 31, no. 02 (2022): 500–527. http://dx.doi.org/10.14295/momento.v31i02.14506.

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Considering Deaf children and adults as bilingual - their first language is a Signed Language (SL) and the second language is learned via print - provides professionals with a paradigm to be used for creating better learning opportunities. In this paper, Greek Sign Language ((G)SL)) [1] as a first language (L1) is the base language we use to present certain bilingual methodological teaching and learning considerations. This work is the result of a long journey from the initial thinking of the American Sign Language Curriculum and its influence on the development of the (G)SL curriculum in Gree

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MAARIF, HARIS AL QODRI. "Adaptive Language Processing Unit for Malaysian Sign Language Synthesizer." IAES International Journal of Robotics and Automation (IJRA) 10, no. 4 (2021): 326. http://dx.doi.org/10.11591/ijra.v10i4.pp326-339.

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Language Processing Unit (LPU) is a system built to process text-based data to comply with the rules of sign language grammar. This system was developed as an important part of the sign language synthesizer system. Sign language (SL) uses different grammatical rules from the spoken/verbal language, which only involves the important words that Hearing/Impaired Speech people can understand. Therefore, it needs word classification by LPU to determine grammatically processed sentences for the sign language synthesizer. However, the existing language processing unit in SL synthesizers suffers time

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Haris, Al Qodri Maarif, Surya Gunawan Teddy, and Akmeliawati Rini. "Adaptive language processing unit for Malaysian sign language synthesizer." IAES International Journal of Robotics and Automation (IJRA) 10, no. 4 (2021): 326–39. https://doi.org/10.11591/ijra.v10i4.pp326-339.

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Language processing unit (LPU) is a system built to process text-based data to comply with the rules of the sign language grammar. This system was developed as an important part of the sign language synthesizer system. Sign language (SL) uses different grammatical rules from the spoken/verbal language, which only involves the important words that hearing/impaired speech people can understand. Therefore, it needs word classification by LPU to determine grammatically processed sentences for the sign language synthesizer. However, the existing language processing unit in SL synthesizers suffers t

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Jantunen, Tommi. "Clausal coordination in Finnish Sign Language." Studies in Language 40, no. 1 (2016): 204–34. http://dx.doi.org/10.1075/sl.40.1.07jan.

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This paper deals with the coordination of clauses in Finnish Sign Language (FinSL). Building on conversational data, the paper first shows that linking in conjunctive coordination in FinSL is primarily asyndetic, whereas in adversative and disjunctive coordination FinSL prefers syndetic linking. Secondly, the paper investigates the nonmanual prosody of coordination: nonmanual activity is shown both to mark the juncture of the coordinand clauses and to draw their contours. Finally, the paper addresses certain forms of clausal coordination in FinSL that are sign language-specific. It is suggeste

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Reddy, Mr G. Sekhar, A. Sahithi, P. Harsha Vardhan, and P. Ushasri. "Conversion of Sign Language Video to Text and Speech." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 159–64. http://dx.doi.org/10.22214/ijraset.2022.42078.

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Abstract: Sign Language recognition (SLR) is a significant and promising technique to facilitate communication for hearingimpaired people. Here, we are dedicated to finding an efficient solution to the gesture recognition problem. This work develops a sign language (SL) recognition framework with deep neural networks, which directly transcribes videos of SL sign to word. We propose a novel approach, by using Video sequences that contain both the temporal as well as the spatial features. So, we have used two different models to train both the temporal as well as spatial features. To train the m

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Herrmann, Annika, and Markus Steinbach. "Sign language and linguistic universals." Sign Language & Linguistics 11, no. 1 (2008): 108–11. http://dx.doi.org/10.1075/sl&l.11.1.14her.

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Teresė, Anželika. "Lithuanian Sign Language Poetry: Location as Mean of Expression of Metaphors." Taikomoji kalbotyra 17 (March 29, 2022): 9–37. http://dx.doi.org/10.15388/taikalbot.2022.17.1.

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In order to solve issues in sign language linguistics, address matters pertaining to maintaining high quality of sign language (SL) translation, contribute to dispelling misconceptions about SL and deaf people, and raise awareness and understand of the deaf community heritage, this article, for the first time in a Lithuanian scientific journal, discusses authentic poetry in Lithuanian Sign Language (LSL) and inherent metaphors that are created by using the phonological parameter – location. The study covered in this article is twofold, involving both the micro-level analysis of metaphors in te

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Said, Yahia, Sahbi Boubaker, Saleh M. Altowaijri, Ahmed A. Alsheikhy, and Mohamed Atri. "Adaptive Transformer-Based Deep Learning Framework for Continuous Sign Language Recognition and Translation." Mathematics 13, no. 6 (2025): 909. https://doi.org/10.3390/math13060909.

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Sign language recognition and translation remain pivotal for facilitating communication among the deaf and hearing communities. However, end-to-end sign language translation (SLT) faces major challenges, including weak temporal correspondence between sign language (SL) video frames and gloss annotations and the complexity of sequence alignment between long SL videos and natural language sentences. In this paper, we propose an Adaptive Transformer (ADTR)-based deep learning framework that enhances SL video processing for robust and efficient SLT. The proposed model incorporates three novel modu

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Alani, Ali A., and Georgina Cosma. "ArSL-CNN: A convolutional neural network for arabic sign language gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1096–107. https://doi.org/10.11591/ijeecs.v22i2.pp1096-1107.

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Sign language (SL) is a visual language means of communication for people with deafness or hearing impairments. In Arabic-speaking countries, there are many arabic sign languages (ArSL) and these use the same alphabets. This study proposes ArSLCNN, a deep learning model that is based on a convolutional neural network (CNN) for translating Arabic SL (ArSL). Experiments were performed using a large ArSL dataset (ArSL2018) that contains 54,049 images of 32 sign language gestures, collected from forty participants. The results of the first experiments with the ArSL-CNN model returned a train and t

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Kose, Hatice, Neziha Akalin, and Pinar Uluer. "Socially Interactive Robotic Platforms as Sign Language Tutors." International Journal of Humanoid Robotics 11, no. 01 (2014): 1450003. http://dx.doi.org/10.1142/s0219843614500030.

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This paper investigates the role of interaction and communication kinesics in human–robot interaction. This study is part of a novel research project on sign language (SL) tutoring through interaction games with humanoid robots. The main goal is to motivate the children with communication problems to understand and imitate the signs implemented by the robot using basic upper torso gestures and sound. We present an empirical and exploratory study investigating the effect of basic nonverbal gestures consisting of hand movements, body and face gestures expressed by a humanoid robot, and having co

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Li, Wenyu, Zhizeng Luo, Wenguo Li, and Xugang Xi. "Chinese sign language recognition based on surface electromyography and motion information." PLOS ONE 18, no. 12 (2023): e0295398. http://dx.doi.org/10.1371/journal.pone.0295398.

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Sign language (SL) has strong structural features. Various gestures and the complex trajectories of hand movements bring challenges to sign language recognition (SLR). Based on the inherent correlation between gesture and trajectory of SL action, SLR is organically divided into gesture-based recognition and gesture-related movement trajectory recognition. One hundred and twenty commonly used Chinese SL words involving 9 gestures and 8 movement trajectories, are selected as research and test objects. The method based on the amplitude state of surface electromyography (sEMG) signal and accelerat

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Axyonov, А., D. Ryumin, and I. Kagirov. "METHOD OF MULTI-MODAL VIDEO ANALYSIS OF HAND MOVEMENTS FOR AUTOMATIC RECOGNITION OF ISOLATED SIGNS OF RUSSIAN SIGN LANGUAGE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 7–13. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-7-2021.

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Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed m

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Ntombela, Silindile, Maleshoane Rapeane-Mathonsi, and Litšepiso Matlosa. "South African Sign Language (SASL) Interpreter Portrayal on SABC 1 News Bulletin: What Do Viewers Think?" African Journal of Inter/Multidisciplinary Studies 6, no. 1 (2024): 1–11. http://dx.doi.org/10.51415/ajims.v6i1.1186.

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Television Sign Language (SL) Interpreters play a vital role in providing the Deaf community with access to information and knowledge in their primary language, Sign Language. This helps the Deaf community stay informed regarding events in their local and global environment, contributing to their development. However, a lack of research exists on SL interpreters on television. With on-screen placement of SL interpreters during news broadcasts being the primary focus in previous studies, this paper attempts to offer a unique contribution from an audience perception examination of South African

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Aziz, Maryam, and Achraf Othman. "Evolution and Trends in Sign Language Avatar Systems: Unveiling a 40-Year Journey via Systematic Review." Multimodal Technologies and Interaction 7, no. 10 (2023): 97. http://dx.doi.org/10.3390/mti7100097.

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Sign language (SL) avatar systems aid communication between the hearing and deaf communities. Despite technological progress, there is a lack of a standardized avatar development framework. This paper offers a systematic review of SL avatar systems spanning from 1982 to 2022. Using PRISMA guidelines, we shortlisted 47 papers from an initial 1765, focusing on sign synthesis techniques, corpora, design strategies, and facial expression methods. We also discuss both objective and subjective evaluation methodologies. Our findings highlight key trends and suggest new research avenues for improving

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Sultan, Ahmed, Walied Makram, Mohammed Kayed, and Abdelmaged Amin Ali. "Sign language identification and recognition: A comparative study." Open Computer Science 12, no. 1 (2022): 191–210. http://dx.doi.org/10.1515/comp-2022-0240.

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Abstract Sign Language (SL) is the main language for handicapped and disabled people. Each country has its own SL that is different from other countries. Each sign in a language is represented with variant hand gestures, body movements, and facial expressions. Researchers in this field aim to remove any obstacles that prevent the communication with deaf people by replacing all device-based techniques with vision-based techniques using Artificial Intelligence (AI) and Deep Learning. This article highlights two main SL processing tasks: Sign Language Recognition (SLR) and Sign Language Identific

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Alani, Ali A., and Georgina Cosma. "ArSL-CNN a convolutional neural network for Arabic sign language gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1096. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1096-1107.

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<p class="IJASEITAbtract">Sign language (SL) is a visual language means of communication for people who are Deaf or have hearing impairments. In Arabic-speaking countries, there are many Arabic sign languages (ArSL) and these use the same alphabets. This study proposes ArSL-CNN, a deep learning model that is based on a convolutional neural network (CNN) for translating Arabic SL (ArSL). Experiments were performed using a large ArSL dataset (ArSL2018) that contains 54049 images of 32 sign language gestures, collected from forty participants. The results of the first experiments with the A

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Shovkovyi, Yevhenii, Olena Grynyova, Serhii Udovenko, and Larysa Chala. "Automatic sign language translation system using neural network technologies and 3D animation." Innovative Technologies and Scientific Solutions for Industries, no. 4(26) (December 27, 2023): 108–21. http://dx.doi.org/10.30837/itssi.2023.26.108.

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Implementation of automatic sign language translation software in the process of social inclusion of people with hearing impairment is an important task. Social inclusion for people with hearing disabilities is an acute problem that must be solved in the context of the development of IT technologies and legislative initiatives that ensure the rights of people with disabilities and their equal opportunities. This substantiates the relevance of the research of assistive technologies, in the context of software tools, such as the process of social inclusion of people with severe hearing impairmen

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Janzen, Terry. "The Grammaticization of Topics in American Sign Language." Studies in Language 23, no. 2 (1999): 271–306. http://dx.doi.org/10.1075/sl.23.2.03jan.

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The topic construction of American Sign Language (ASL), within a topic-comment discourse structure framework, is explained as having emerged from gestural, communicative roots. In modern ASL, the prototypical topic construction is understood to grammatically mark pragmatic information that is accessible to both the signer and the addressee. But the construction is shown to have grammaticized further, with grammatical meaning having to do with text organization and with no reference to pragmatic, extra-linguistic information. The topic, however, is not seen as grammaticizing into a subject. Rat

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Trivedi, Kaustubh, Priyanka Gaikwad, Mahalaxmi Soma, Komal Bhore, and Prof Richa Agarwal. "Improve the Recognition Accuracy of Sign Language Gesture." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4343–47. http://dx.doi.org/10.22214/ijraset.2022.43220.

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Abstract: Image classification is one of classical issue of concern in image processing. There are various techniques for solving this issue. Sign languages are natural language that used to communicate with deaf and mute people. There is much different sign language in the world. But the main focused of system is on Sign Language (SL) which is on the way of standardization in that the system will concentrated on hand gestures only. Hand gesture is very important part of the body for exchange ideas, messages, and thoughts among deaf and dumb people. The proposed system will recognize the numbe

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Abbas, Ali, Summaira Sarfraz, and Umbreen Tariq. "Pakistan sign language translation tool in educational setting: teachers perspective." Journal of Enabling Technologies 16, no. 1 (2022): 38–47. http://dx.doi.org/10.1108/jet-06-2021-0033.

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PurposeThe current study aims to determine the viability of the tool developed by Abbas and Sarfraz (2018) to translate English speech and text to Pakistan Sign Language (PSL) with bilingual subtitles.Design/methodology/approachFocus group interviews of 30 teachers of a Pakistani private university were conducted; who used the PSL translation tool in their classrooms for lecture delivery and communication with the deaf students.FindingsThe findings of the study determined the viability of the developed tool and showed that it is helpful in teaching deaf students efficiently. With the availabil

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Gaikwad, Priyanka, Kaustubh Trivedi, Mahalaxmi Soma, Komal Bhore, and Prof Richa Agarwal. "A Survey on Sign Language Recognition with Efficient Hand Gesture Representation." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 21–25. http://dx.doi.org/10.22214/ijraset.2022.41963.

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Abstract: Image classification is one amongst classical issue of concern in image processing. There are various techniques for solving this issue. Sign languages are natural language that want to communicate with deaf and mute people. There's much different sign language within the world. But the most focused of system is on Sign language (SL) which is on the way of standardization there in the system will focused on hand gestures only. Hand gesture is extremely important a part of the body for exchange ideas, messages, and thoughts among deaf and dumb people. The proposed system will recogniz

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Hao, Zhanjun, Yu Duan, Xiaochao Dang, Yang Liu, and Daiyang Zhang. "Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information." Sensors 20, no. 14 (2020): 4025. http://dx.doi.org/10.3390/s20144025.

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In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition meth

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Briones Cerquín, Angel Diego, Johan Alonso Tumay Guevara, and Christian Ovalle. "Mobile Application for Continuous Recognition and Classification of Sign Language Images through Deep Learning." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 07 (2025): 4–21. https://doi.org/10.3991/ijim.v19i07.52853.

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Throughout the world, sign languages (SL) present significant challenges for effective communication in everyday environments and technological applications. In the field of SL recognition (SLR) using artificial intelligence (AI), two approaches have been developed: isolated SLR (ISLR) and continuous SLR (CSLR). To overcome the limitations of CSLR in SL, we developed a mobile application that integrates an AI-based algorithm in Python, designed to capture and analyze sign sequences through the device’s camera. The application facilitates the creation of a continuous database containing 14 dyna

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Li, Wenguo, Zhizeng Luo, and Xugang Xi. "Movement Trajectory Recognition of Sign Language Based on Optimized Dynamic Time Warping." Electronics 9, no. 9 (2020): 1400. http://dx.doi.org/10.3390/electronics9091400.

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Movement trajectory recognition is the key link of sign language (SL) translation research, which directly affects the accuracy of SL translation results. A new method is proposed for the accurate recognition of movement trajectory. First, the gesture motion information collected should be converted into a fixed coordinate system by the coordinate transformation. The SL movement trajectory is reconstructed using the adaptive Simpson algorithm to maintain the originality and integrity of the trajectory. The algorithm is then extended to multidimensional time series by using Mahalanobis distance

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Pirot, Khunaw Sulaiman, and Wirya Izzaddin Ali. "The Phonological Structure of American Sign Language -ASL and zmânî âmâžaî kurdî - ZAK." Journal of University of Raparin 9, no. 4 (2022): 155–87. http://dx.doi.org/10.26750/vol(9).no(4).paper8.

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This paper deals primarily with the phonological structure of American Sign Language (ASL) and zmânî âmâžaî kurdî (ZAK) -Kurdish Sign Language. It is concerned with sign language (SL) and the types of sign language. One type is primary sign languages which are used by the Deaf people. Sign language is a visual-gestural language which relies on the use of the hands, facial expressions and body movements.Generally, there are myths about SLs. People believe that SLs are universal and have no grammatical structure. However, sign languages, as spoken languages, have lexicon, phonology, morphology a

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Guimarães, Cayley, Milton César Oliveira Machado, and Sueli F. Fernandes. "Comic Books: A Learning Tool for Meaningful Acquisition of Written Sign Language." Journal of Education and Learning 7, no. 3 (2018): 134. http://dx.doi.org/10.5539/jel.v7n3p134.

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Deaf people use Sign Language (SL) for intellectual development, communications and other human activities that are mediated by language—such as the expression of complex and abstract thoughts and feelings; and for literature, culture and knowledge. The Brazilian Sign Language (Libras) is a complete linguistic system of visual-spatial manner, which requires an adequate writing system. The specificities of Libras pose a challenge for alphabetisation/literacy in the educational process of the Deaf, which allows for meaning attribution by the Deaf learner only when the SL is the central pedagogic

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Tilves Santiago, Darío, Carmén García Mateo, Soledad Torres Guijarro, Laura Docío Fernández, and José Luis Alba Castro. "Estudio de bases de datos para el reconocimiento automático de lenguas de signos." Hesperia: Anuario de Filología Hispánica 22 (March 13, 2020): 145–60. http://dx.doi.org/10.35869/hafh.v22i0.1658.

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Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datase

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Tilves Santiago, Darío, Carmén García Mateo, Soledad Torres Guijarro, Laura Docío Fernández, and José Luis Alba Castro. "Estudio de bases de datos para el reconocimiento automático de lenguas de signos." Hesperia: Anuario de Filología Hispánica 23 (March 13, 2020): 145–60. http://dx.doi.org/10.35869/hafh.v23i0.1658.

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Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datase

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Lehmann, Christian. "Roots, stems and word classes." Parts of Speech: Descriptive tools, theoretical constructs 32, no. 3 (2008): 546–67. http://dx.doi.org/10.1075/sl.32.3.04leh.

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The assignment of a linguistic sign to a word class is an operation that must be seen as part of the overall transformation of extralinguistic substance into linguistic form. In this, it is comparable to such processes as the transitivization of a verbal base, which further specifies a relatively rough categorization. Languages differ both in the extent to which they structure the material by purely grammatical criteria and in the level at which they do this. The root and the stem are the lowest levels at which a linguistic sign can be categorized in terms of language-specific structure. Furth

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H. Mahmutović, Esad, and Husnija Hasanbegović. "FORMS OF HAND IN SIGN LANGUAGE IN BOSNIA AND HERZEGOVINA – PRACTICAL EXAMPLES." Journal Human Research in Rehabilitation 4, no. 2 (2014): 25–29. http://dx.doi.org/10.21554/hrr.091401.

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In this work there is shown illustration of 14 newly discovered forms of hand in sign language (SL) in B&H, and one photographic presentation for each of the signs in which that form of the hand is represented. The goal of this work was to visualize the newly discovered forms of hand in order to ensure their adequate practical application in everyday communication of the deaf, various studies of sign language and future scientific analysis. Considering that the analysis of the contents in study is encompassing only 425 signs SL in B&H, it can be concluded that future linguistic researc

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Jefwa Mweri. "Aspects of Kenyan sign language (KSL) morphology." International Journal of Science and Research Archive 8, no. 2 (2023): 207–26. http://dx.doi.org/10.30574/ijsra.2023.8.2.0150.

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Morphology examines forms of words and their relationship with other words in existence in a language. Generally, in linguistics, morphology studies how words are formed. However, in signed linguistics, morphology does not just study word formation per se (since SL uses signs) but rather how language makes use of smaller units that are important to construct larger meaningful units or signs. Accordingly, sign language morphology deals with how to put together sign components that are meaningful to construct complex signs. This paper sets out to examine the way that components of signs are put

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Bigand, Félix, Elise Prigent, Bastien Berret, and Annelies Braffort. "Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach." PLOS ONE 16, no. 10 (2021): e0259464. http://dx.doi.org/10.1371/journal.pone.0259464.

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Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were

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Asiri, Mashael M., Abdelwahed Motwakel, and Suhanda Drar. "Robust sign language detection for hearing disabled persons by Improved Coyote Optimization Algorithm with deep learning." AIMS Mathematics 9, no. 6 (2024): 15911–27. http://dx.doi.org/10.3934/math.2024769.

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<abstract> <p>Sign language (SL) recognition for individuals with hearing disabilities involves leveraging machine learning (ML) and computer vision (CV) approaches for interpreting and understanding SL gestures. By employing cameras and deep learning (DL) approaches, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), these models analyze facial expressions, hand movements, and body gestures connected with SL. The major challenges in SL recognition comprise the diversity of signs, differences in signing styles, and the need to recognize the context in w

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Krause, Christina C., and Annika M. Wille. "Sign Language in Light of Mathematics Education: An Exploration Within Semiotic and Embodiment Theories of Learning Mathematics." American Annals of the Deaf 166, no. 3 (2021): 352–77. https://doi.org/10.1353/aad.2021.0025.

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Research rarely focuses on how deaf and hard of hearing (DHH) students address mathematical ideas. Complexities involved in using sign language (SL) in mathematics classrooms include not just challenges, but opportunities that accompany mathematics learning in this gestural-somatic medium. The authors consider DHH students primarily as learners of mathematics, and their SL use as a special case of language in the mathematics classroom. More specifically, using SL in teaching and learning mathematics is explored within semiotic and embodiment perspectives to gain a better understanding of how u

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Kang, Xinchen, Dengfeng Yao, Minghu Jiang, Yunlong Huang, and Fanshu Li. "Semantic Network Model for Sign Language Comprehension." International Journal of Cognitive Informatics and Natural Intelligence 16, no. 1 (2022): 1–19. http://dx.doi.org/10.4018/ijcini.309991.

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In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on cognitive functionalities in human language processing. The semantic network model (SNM) that represents semantic relations between concepts is used as a form of knowledge representation. The proposed model is applied in the comprehension of sign language for classifier predicates. The spreading activation search method is initiated by labeling a set of source nodes (e.g., concepts in the semantic network) with weights or “activa

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Tamimi Sa'd, Seyyed Hatam, and Ronnie B. Wilbur. "Basic clause negator in Sadat Tawaher Sign Language." Proceedings of the Linguistic Society of America 10, no. 1 (2025): 5903. https://doi.org/10.3765/plsa.v10i1.5903.

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Sign languages (SLs) generally have several manual signs to negate sentences, usually with one sign serving as the basic clause negator and with its function being only to reverse the polarity of a clause without adding any additional semantic content. We identify the basic clause negator in Sadat Tawaher Sign Language (STSL), a SL that emerged in a single household in a small Iranian village around sixty years ago. While STSL has several manual negators, all of which may serve as sentential negators, we argue that one negator, NEGbasic, is the basic clause negator. The data includes both isol

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Mora, Laura, Anna Sedda, Teresa Esteban, and Gianna Cocchini. "The signing body: extensive sign language practice shapes the size of hands and face." Experimental Brain Research 239, no. 7 (2021): 2233–49. http://dx.doi.org/10.1007/s00221-021-06121-9.

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AbstractThe representation of the metrics of the hands is distorted, but is susceptible to malleability due to expert dexterity (magicians) and long-term tool use (baseball players). However, it remains unclear whether modulation leads to a stable representation of the hand that is adopted in every circumstance, or whether the modulation is closely linked to the spatial context where the expertise occurs. To this aim, a group of 10 experienced Sign Language (SL) interpreters were recruited to study the selective influence of expertise and space localisation in the metric representation of hand

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López, Juan Pedro, Marta Bosch-Baliarda, Carlos Alberto Martín, et al. "Design and development of sign language questionnaires based on video and web interfaces." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 4 (2019): 429–41. http://dx.doi.org/10.1017/s0890060419000374.

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AbstractConventional tests with written information used for the evaluation of sign language (SL) comprehension introduce distortions due to the translation process. This fact affects the results and conclusions drawn and, for that reason, it is necessary to design and implement the same language interpreter-independent evaluation tools. Novel web technologies facilitate the design of web interfaces that support online, multiple-choice questionnaires, while exploiting the storage of tracking data as a source of information about user interaction. This paper proposes an online, multiple-choice

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Russo Cardona, Tommaso. "Metaphors in sign languages and in co-verbal gesturing." Dimensions of gesture 8, no. 1 (2008): 62–81. http://dx.doi.org/10.1075/gest.8.1.06rus.

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In analyses of the grammatical structure of sign languages (Liddell, 2003), “classifier forms” which play a major role in these spatialised grammars, are looked upon as a “gestural” component of sign language. Kendon (2004) pointed out that some of the organizational principles of co-verbal gesturing can be compared to “classifiers” in sign languages. In this paper drawing on previous analyses of LIS (Italian Sign Language) metaphors in discourse (Russo, 2004a, 2005) the role of “classifier forms” in SL metaphors is examined and compared with some aspects of gestural metaphors produced during

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Miah, Abu Saleh Musa, Md Al Mehedi Hasan, Si-Woong Jang, Hyoun-Sup Lee, and Jungpil Shin. "Multi-Stream General and Graph-Based Deep Neural Networks for Skeleton-Based Sign Language Recognition." Electronics 12, no. 13 (2023): 2841. http://dx.doi.org/10.3390/electronics12132841.

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Sign language recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. However, due to the complex background, light illumination, and subject structures in videos, researchers still face challenges in developing effective SLR systems. Many researchers have recently sought to develop skeleton-based sign language recognition systems to overcome the subject and background variation in hand gesture sign videos. However, skeleton-based SLR is still under exploration, mainly due to a lack of information and hand key point annotations. More rec

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