Sign Language Research Articles

Enhancing anatomy education using anatomical sign language: expanding its expressive capabilities to the upper and lower extremities, face, and brain structures.

Various information technologies have been introduced for anatomy education in the current digital era, including three-dimensional (3D) virtual reality, mobile augmented reality, and 3D printing. While these technologies enhance educational effectiveness, their high cost often restricts their accessibility. Conversely, low-cost methods using everyday items have proven effective in anatomy education. The anatomical sign language (ASL) method has been introduced, and uses the fingers, hands, and arms to represent anatomical structures to leverage muscle memory to aid the retention and understanding of complex anatomical structures and provide a comprehensive and interactive approach to anatomy education. This study was performed to expand ASL to include the expressive capabilities of the upper and lower extremities, and the face and brain. The results indicate that ASL effectively illustrates the anatomy of various structures. The educational benefits of ASL for anatomy and radiologic anatomy education are discussed.

The impact of sign language on Spanish language acquisition

The present research article examines the role of sign language in the acquisition of the Spanish language through the teaching of sign language at the high school level. It focuses on a sample of high school students from a private institution in the city of Chone. Additionally, a descriptive approach was employed, centered on the scientific tasks of research and the respective sampling method. For data collection, two interviews were conducted with English teachers, and a survey was applied to an English teacher. The results indicate that the acquisition of the Spanish language through the use of sign language is significant for students. Moreover, the use of pedagogical strategies is essential to help them develop their communicative skills. On the other hand, it is important for teachers to use visual resources to ensure active participation from students. Consequently, the findings highlight factors influencing the learning and acquisition of the Spanish language through the use of sign language. In other words, this study contributes to educational research by providing relevant information about the importance of Spanish language acquisition for students with hearing impairments.

The Impact of Sign Language on Spanish Language Acquisition

The present research article examines the role of sign language in the acquisition of the Spanish language through the teaching of sign language at the high school level. It focuses on a sample of high school students from a private institution in the city of Chone. Additionally, a descriptive approach was employed, centered on the scientific tasks of research and the respective sampling method. For data collection, two interviews were conducted with English teachers, and a survey was applied to an English teacher. The results indicate that the acquisition of the Spanish language through the use of sign language is significant for students. Moreover, the use of pedagogical strategies is essential to help them develop their communicative skills. On the other hand, it is important for teachers to use visual resources to ensure active participation from students. Consequently, the findings highlight factors influencing the learning and acquisition of the Spanish language through the use of sign language. In other words, this study contributes to educational research by providing relevant information about the importance of Spanish language acquisition for students with hearing impairments.

Language Complexities for Deaf and Hard of Hearing Individuals in Their Pursuit of a Career in Science, Technology, Engineering, Mathematics, and Medicine: Perspectives From an LSL/ASL User.

A research scientist with 35 years of experience in the field of hearing research, the author writes that his own experiences have provided a perspective that may be valuable for both future d/Deaf and Hard of Hearing (D/HH) individuals and their peers with typical hearing in their pursuit of a career in Science, Technology, Engineering, Mathematics, and Medicine (STEMM). The author first describes the role of Hearing Inclusive-Association for Research in Otolaryngology in enhancing inclusivity and accessibility for D/HH scientists in the field of Hearing Research. Second, the challenges faced by D/HH scientists arise from the difficulties of working with peers with typical hearing, resulting in less inclusivity and accessibility for the D/HH scientists. The next section deals with solutions to these challenges, including American Sign Language interpreters, websites that give advice on inclusivity and accessibility, and technological advances such as assistive listening devices and smartphones with a capacity for auto captioning. The solutions, however, are fraught with issues such as limited budgets and misperception. Fourth, the author argues that the experiences necessary for a career in STEMM may require a higher-than-expected degree of collaboration with peers with typical hearing outside the laboratory. Finally, studies on successful D/HH scientists in STEMM fields should include experiences of obtaining successful research funding.

SMART SIGN: Live Sign Language Interpretation For Barrier-Free Video Conferencing

In the digital era, effective and inclusive communication is essential, particularly in video conferencing platforms widely adopted post-pandemic for professional and social interactions. However, these platforms often lack accessibility features for individuals with hearing and speech impairments. This research presents a lightweight, real-time sign language translator designed for seamless integration with popular video conferencing platforms like Google Meet, Microsoft Teams, Zoom, and Discord. Utilizing deep learning algorithms, image processing techniques, and virtual camera technology, the system translates sign language into written captions, displayed in real-time. By fostering accessibility and inclusivity, this innovation empowers deaf and mute individuals to participate actively in virtual meetings, bridging communication gaps and advancing equal opportunities for all.

The Effectiveness of an Authentic E-Learning Environment for Deaf Learners

The digitalization of educational systems and the corresponding impact on the learners’ improvement require modern pedagogical approaches to increase motivation among learners in distance learning. According to the literature, authentic e-learning and real-world themes create a dynamic learning setting and enhance learners’ engagement; however, the impact of adopting authentic learning has not been investigated for deaf learners in a sign language e-learning setting. Therefore, this study aims to examine the effect of adopting authentic learning for the design of e-learning for deaf learners. The study employed a mixed-methods approach by conducting a one-group pre-test–post-test with 11 deaf learners and measuring design principles of authentic learning via semi-structured interviews. The statistical portion analyzed the T-test and Cohen’s d-effect size, and the result showed that e-learning through authentic themes was significantly effective. The interview results revealed a positive attitude toward using e-learning based on an authentic learning approach, which increased deaf learners’ motivation. It was found that e-learning based on an authentic sign language e-learning setting and technology enhances deaf learners’ satisfaction and motivation with their first e-learning experience.

Grammaticalization of a motion verb into an imperative marker in two sign languages

grammaticalization of a motion verb into an imperative marker in two sign languages

Contextual Translation System to Sign Language

People with hearing and speech disabilities face significant challenges in communicating with others, as not everyone understands sign language. This project aims to create a system that helps bridge this communication gap by converting spoken English into Indian Sign Language (ISL). The system works by recognizing voice input, the recognized speech is converted into text, which is then simplified using natural language processing techniques. Finally, the text is translated into ISL and displayed as a series of images or motion videos using Python libraries. This system provides an easy and accessible way for people with hearing or speech disabilities to communicate effectively, promoting inclusivity and understanding in everyday interactions.

Comparative study on articulatory ease in three sign languages: Libras, LSCu, and Cena

comparative study on articulatory ease in three sign languages: Libras, LSCu, and Cena

Does it need to be double and novel? Investigating metaphorical signs in German Sign Language

Does it need to be double and novel? Investigating metaphorical signs in German Sign Language

How linear movements shape quantification in Chinese Sign Language (CSL)

How linear movements shape quantification in Chinese Sign Language (CSL)

Transitive and unaccusative V-V constructions in Japanese Sign Language

Transitive and unaccusative V-V constructions in Japanese Sign Language

Phonological realizations of three types of compounds in Japanese Sign Language

Phonological realizations of three types of compounds in Japanese Sign Language

Phonemic status of the O handshape in Japanese Sign Language

Phonemic status of the O handshape in Japanese Sign Language

Real-Time Sign Language Interpretation for Inclusive Communication

Sign language plays a crucial role as a communication tool for both the deaf and hard-of- hearing communities, enabling them to engage and interact effectively within their own community as well as with others.However, communication barriers arise when individuals unfamiliar with sign language engage with those who rely on it, underscoring the need for inclusive solutions. Real-time sign language interpretation systems, leveraging machine learning and computer vision technologies, present a promising approach to bridging this gap. These systems convert sign language gestures into spoken or written language by utilizing gesture recognition algorithms, neural networks, and natural language processing. By analyzing hand movements, facial expressions, and body language, the systems provide accurate, context-aware translations of various sign languages, such as American Sign Language (ASL), with minimal delay. This enables seamless, natural interactions, making such technologies essential for fostering inclusive communication in diverse settings. Key Words: Sign language recognition, real- time interpretation, machine learning, com- puter vision, gesture recognition, neural networks, natural language processing, communication barriers, inclusivity, American Sign Language (ASL).

IN PUBLICA COMMODA, CREATING BARRIER-FREE EDUCATIONAL STATISTICS VIDEOS FOR HIGHER EDUCATION: INSIGHTS AND EVIDENCE FROM DEAF PEOPLE USING GERMAN SIGN LANGUAGE (DGS)

To address diversity-sensitive higher education, we provide the first results exhibiting the importance of the use of German Sign Language (Deutsche Gebärdensprache, DGS) in lecture videos of elementary statistics courses. We examined whether deaf individuals preferred lecture videos in DGS over those with captions. Results from quantitative and qualitative analyses on the basis of survey data and interview material in DGS indicate a clear preference for teaching materials in DGS. While the limited sample size (n = 10) did not allow for detailed modeling of the underlying reasoning, this study puts forward some indicative results and directions for future research, like assessing objective learning outcomes by employing pre- and post?tests and employing eye-tracking for more in-depth analysis of learning behaviors.

AzSLD: Azerbaijani sign language dataset for fingerspelling, word, and sentence translation with baseline software.

Advancements in sign language processing technology hinge on the availability of extensive, reliable datasets, comprehensive instructions, and adherence to ethical guidelines. To facilitate progress in gesture recognition and translation systems and to support the Azerbaijani sign language community we present the Azerbaijani Sign Language Dataset (AzSLD). This comprehensive dataset was collected from a diverse group of sign language users, encompassing a range of linguistic parameters. Developed within the framework of a vision-based Azerbaijani Sign Language translation project, AzSLD includes recordings of the fingerspelling alphabet, individual words, and sentences. The data acquisition process involved recording signers across various age groups, genders, and proficiency levels to ensure broad representation. Sign language sentences were captured using two cameras from different angles, providing comprehensive visual coverage of each gesture. This approach enables robust training and evaluation of gesture recognition algorithms. The dataset comprises 30,000 meticulously annotated videos, each labeled with precise gesture identifiers and corresponding linguistic translations. To facilitate efficient usage of the dataset, we provide technical instructions and source code for a data loader. Researchers and developers working on sign language recognition, translation, and synthesis systems will find AzSLD invaluable, as it offers a rich repository of labeled data for training and evaluation purposes.

Swin-MSTP: Swin transformer with multi-scale temporal perception for continuous sign language recognition

Continuous sign language recognition (CSLR) aims to recognize and interpret sequences of sign language gestures in videos. Currently, most CSLR frameworks combine spatial feature extractors based on convolutional neural networks (CNNs) with temporal convolutional networks (TCNs) for sequence learning. However, CNN-based spatial feature extractors apply the same convolutional kernel uniformly across all regions of an image, which limits their capacity to extract complex details such as fingers and facial features, which are essential for CSLR. In addition, sign languages include signs of varying lengths that cannot be accurately modeled using a fixed-size TCN. To address these issues, we present the Swin multiscale temporal perception (Swin-MSTP) framework, in which the Swin Transformer is utilized as the spatial feature extractor, capable of capturing fine spatial details and providing a stronger contextual understanding between sign language elements in video frames. The Swin Transformer was integrated with the MSTP module to extract time-wise features. Experimental results show that our single-modality system outperformed existing methods on the CSL dataset, including multimodal frameworks. The model also achieved competitive performance on the Phoenix2014, Phoenix2014-T, and CSL-Daily datasets. The code is available at https://github.com/Hamzah-Luqman/SWIN-MSTP.

The dynamic Colombian sign language dataset for basic conversation LSC70.

Sign language is a form of non-verbal communication used by people with hearing disability. This form of communication relies on the use of signs, gestures, facial expressions, and more. Considering that in Colombia, the population with hearing impairments is around half a million, a database of dynamic, alphanumeric signs and commonly used words was created to establish a basic conversation. For this purpose, 70 non-expert volunteers in Colombian Sign Language participated, and they were recorded against a clear background under uncontrolled lighting and clothing conditions. The dataset was named LSC70 and includes a six-frame transition in JPG format. It is organized into three parts: The first part contains alphanumeric signs at a resolution of 640×480 pixels; the second part includes word signs at a size of 640×480 pixels; and the third part focuses on the dominant hand in alphanumeric signs at 120×120 pixels. Through filtering that removed similar signs, the dataset now contains 35,208 frames with a total of 47 different signs. These images have been validated by experts at the University of Cauca and are available for free use within the research community.

Intellectual mapping of the sign language role in deaf education: forecasting future reforms

Intellectual mapping of the sign language role in deaf education: forecasting future reforms