Deaf Community Research Articles

Hear our voices: The perceptions and experiences of women who are Deaf on gender-based violence

Background: While all women are at risk of gender-based violence (GBV), it is essential to acknowledge that women are not a homogenous group and that women who are Deaf may experience GBV differently. This study aimed to answer the question: What are the perceptions and experiences of GBV among women who are Deaf?Objectives: The study’s objectives were to explore and describe these women’s understanding of GBV terminology, their perceptions of GBV and challenges regarding support strategies in place to respond to their unique circumstances.Method: A qualitative study with four workshops was conducted with 60 participants. The data collected were analysed using thematic analysis.Results: The results yielded three themes that intrinsically spoke to women’s understanding of GBV terminology, perception of GBV and challenges accessing support. The results highlight that women who are Deaf are at a greater risk of GBV. Participants emphasised the importance of exploring the perceptions of GBV among men who are Deaf. Findings also indicated women’s challenges when seeking support.Conclusion: The findings underscore the necessity of offering specific assistance to Deaf women facing GBV. Based on the study findings, it can be concluded that those providing support in GBV matters should receive specialised training in GBV, including Deaf culture and South African sign language.Contribution: The study findings contribute to the field of disability and the development of effective GBV strategies and programmes that are inclusive of women who are Deaf within a South African context.

Word of mouth: Mouthing patterns in a bimodal multilingual deaf community

Word of mouth: Mouthing patterns in a bimodal multilingual deaf community

SIGN-LANGUAGE TRANSLATOR USING MACHINE LEARNING

This research introduces a cutting-edge system designed to facilitate communication between individuals who are deaf and those who are hearing. By employing sophisticated machine learning algorithms and specific convolutional neural networks (CNNs), the proposed system can effectively sign language gestures into text or speech. A meticulously curated collection of sign language gestures serves as the foundation for training the model, ensuring its proficiency in accurately classifying several hand shapes and positions. To optimize classification performance, the system incorporates data preprocessing techniques that highlight the most distinctive features of the hands, thereby streamlining the computational process. This paper provides a comprehensive overview of the system's architecture, training methodology, and evaluation results, emphasizing the critical role of machine learning in developing inclusive communication tools that empower the deaf community. Future research initiatives will focus's on expanding the gesture dataset and refining real-time processing capabilities to further enhance the system's effectiveness and accessibility. Key Words: CNN, TensorFlow, Keras, NLTK

Advanced Hybrid Neural Networks for Accurate Recognition of the Extended Alphabet and Dynamic Signs in Mexican Sign Language (MSL)

The Mexican deaf community primarily uses Mexican Sign Language (MSL) for communication, but significant barriers arise when interacting with hearing individuals unfamiliar with the language. Learning MSL requires a substantial commitment of at least 18 months, which is often impractical for many hearing people. To address this gap, we present an MSL-to-Spanish translation system that facilitates communication through a spelling-based approach, enabling deaf individuals to convey any idea while simplifying the AI’s task by limiting the number of signs to be recognized. Unlike previous systems that focus exclusively on static signs for individual letters, our solution incorporates dynamic signs, such as “k”, “rr”, and “ll”, to better capture the nuances of MSL and enhance expressiveness. The proposed Hybrid Neural Network-based algorithm integrates these dynamic elements effectively, achieving an F1 score of 90.91%, precision of 91.25%, recall of 91.05%, and accuracy of 91.09% in the extended alphabet classification. These results demonstrate the system’s potential to improve accessibility and inclusivity for the Mexican deaf community.

Sign Language to Speech Conversion

Human communication relies heavily on verbal and non-verbal cues, with sign language serving as a crucial method of interaction for individuals with hearing impairments. However, the communication barrier between sign language users and those unfamiliar with it remains a significant challenge. This paper presents an innovative approach to bridging this gap through an Arduino-based system that converts sign language gestures directly into speech without the use of a camera. By utilizing flex sensors and an accelerometer, this system detects hand gestures and movements, processes this data, and produces corresponding audio output. This approach offers a more accessible and portable solution compared to traditional camera-based systems, potentially revolutionizing real-time communication for the deaf community. The experimental results demonstrate a gesture recognition accuracy of 92% and a response time of under 500 milliseconds, indicating the system's viability for practical applications

IoT Based Sign to Speech Converter System

The idea introduces a system for facilitating communication between the deaf and the non-signing community through a wearable device called IoT based Sign-to-Speech Converter System. The System consist of glove & it utilizes Internet of Things (IoT) technology to bridge the communication gap by translating sign language (gestures) into spoken language in real-time. The core functionality of the system is augmented by seamless integration with an Android application, enhancing user experience and accessibility. The proposed system leverages a combination of sensors embedded within the glove to capture intricate hand movements and gestures commonly used in sign language. These data are transmitted wirelessly to a central processing unit, to interpret the gestures and generates corresponding spoken language output. The Android application serves as a user interface, allowing for customization of settings and additional features such as text-based communication. Effectiveness and practicality of the proposed system have been demonstrated, opening-up new avenues for inclusive communication and empowerment of the deaf community.

Translate Arabic Text to Arabic Gloss for Sign Language

Arabic Sign Language (ArSL) is a language used by the deaf community across Arab countries, but the lack of familiarity with ArSL among the hearing population often leads to social isolation for deaf individuals. The structural differences between ArSL and spoken Arabic pose significant challenges for machine translation. In this study, we enhance Arabic to ArSL gloss translation by employing data augmentation techniques, expanding the dataset from 600 to over 23,328 samples using sequence-to-sequence transformer models. Our approach achieved a substantial performance improvement, increasing the BLEU score from 11.1% in the baseline model to 52.72% on original test set. The best model achieved a BLEU score of 85.17% on augmented data test, underscoring the effectiveness of data augmentation in enhancing ArSL translation quality.

Deafness and Inclusion: Perceived Communicative Barriers in the Area of Rehabilitation in Italy

Abstract Introduction Deafness can create a communication deficit/barrier between the Deaf or Hard of Hearing patient and the Healthcare Workers (HWs), with negative outcomes for diagnostic and therapeutic success. As underlined by the COVID-19 pandemic, there is a need to train HWs in effective communication strategies in order to avoid marginalising or excluding the Deaf Community by denying them access to healthcare services. This study highlights the urgent need and desire of Healthcare staff for training in effective communication strategies, such as sign language, to help the Deaf community gain equal access to Healthcare. Materials and methods A cross-sectional survey was carried out between May and July 2022 using the Google Forms platform. An anonymous questionnaire was given to Rehabilitation HWs (RHWs) at a national level to collect information from the HWs of the various areas of rehabilitation, identifying their knowledge, training, and experience with regard to deafness. Data analysis was then carried out using the Google Forms program, SPSS program Windows7 Excel Spreadsheet. Results 252 RHWs belonging to 8 different profiles answered this survey. 89% were women. 46% were younger than 30. 57% of these 252 had deaf patients, but 54.6% of these had no prior training. 33% of the RHWs had followed courses or seminars on deafness at least once. When asked, 94% were interested in courses or training in communication with deaf people. Conclusions HWs recognize the gap in communication and are willing to learn more in the matter for effective communication with their patients to gain a better therapeutic success. They also consider a program that imparts such knowledge and training to be highly important. Key messages • Therapeutic success is increased if there are effective communication strategies, such as sign language, between the Deaf and Healthcare provider. • This study shows the importance of training all health workers in effective communication strategies with deaf people to allow an all-inclusive healthcare.

SignifAI: French sign language translation based on deep learning and large language models

In this project, we propose SignifAI which is an AI-powered software that translates French Sign Language into spoken French. The goal of SignifAI is to remove the language barrier that separates the deaf and hearing communities. The translation process proves to be a major challenge for existing methods, as most of them rely on traditional Natural Language Processing (NLP) methods which solve the task in a similar way as spoken languages (e.g., paying more attention to the logics and reasoning). The drastic syntactical difference between sign and spoken languages, however, makes such approaches not as effective and efficient. In SignifAI, we propose to leverage a combination of deep learning models for sign gesture recognition and large language models (LLM) for sentence generation from recognized words. The former allows for precise detection of individual words, while the latter takes the recognized words as inputs and returns a fluently reformulated translated sentence. With extremely limited data available for French sign language, we created our own enhanced dataset through synthetically augmenting existing videos. Utilizing the SlowFast model for this task, we have achieved a maximum test accuracy of 98.9%, and a fluid translation process after implementing ChatGPTs API. The potential applications of SignifAI range from aiding communication between the deaf and the hearing, to helping the deaf place phone calls in numerical services. Ultimately, SignifAI strives to improve all aspects of a deaf persons life, and compensates for their speaking disabilities.

Enhanced BERT-based Multi-Head Self-Attention Transformer for Transformation of Marathi Text to Marathi Sign Language Gloss

One recent advancement in the field of machine learning is the translation of text into sign language gloss, which is a form of natural language for the deaf community. The work presented is a new method to translate Marathi text to Marathi sign language gloss by combining salient features of Bidirectional Encoder Representation from Transformer (BERT) for tokenization and complementing the tokenized frame with Encoder with Attention mechanism and decoding with the LSTM decoder. The work conducted experiments on the created corpora of Marathi text and Marathi sign language gloss sentence pairs. The experiments that employed three models show that the suggested model performs better than the existing approaches. The results show that the translation of Marathi text to sign gloss achieves improved performances with an accuracy of 91.5% and the BLEU scores BLEU-1: 85, BLEU-2: 75, BLEU-3: 65, and BLEU-4: 57.

LÍNGUA BRASILEIRA DE SINAIS: BREVE HISTÓRICO DOS ASPECTOS EDUCACIONAIS DOS SURDOS

This article aims to present a compendium of the evolutionary history of Brazilian Sign Language (Libras) and its reflections on the deaf community, particularly concerning education for all. Schooling must be ensured for everyone, and therefore, addressing any disparities caused by specific needs is crucial. This dynamism reflects the shared responsibility of public and private agencies within legislative and educational contexts to provide specific training related to the language and prepare the environment for promoting teaching and learning. The method used involved exploratory bibliographic research in scientific articles and books, with filters for the central theme of this work. The results reveal a concerning state of involvement and interaction of public policies and effective actions for the integration and inclusion of deaf individuals in social and educational contexts, highlighting the challenge for more in-depth research on the topic to promote solutions and address the social disparities faced by these individuals.

Enhancing sign language recognition and accessibility for the deaf community in China

This paper explores the critical role of sign language in enabling communication and social inclusion for the deaf community in China, where approximately 27.8 million individuals experience hearing impairments. Despite the recognition of linguistic human rights, Chinese law lacks formal acknowledgment of sign language, leading to fragmented regulations and insufficient support. The study highlights deficiencies in existing legislation and proposes recommendations for enhancing legal frameworks to protect and promote the rights of deaf individuals. Additionally, the paper examines the disparities between local and national sign languages and underscores the potential of technological advancements, such as sign language translation gloves, in bridging communication gaps and fostering social integration.

Information behaviour of Deaf people in university libraries

In Brazil, there are more than 10 million Deaf people. Of these, 2,592 Deaf people are enrolled in Higher Education institutions. This pilot study aims to explore how the field of Library and Information Science (LIS) has studied Deaf people in university libraries. This research uses a mixed methods approach: the first part of the study uses a descriptive approach to present the current state of LIS literature on Deaf users of university libraries. The second part of the research is empirical and consists of a survey that has yet to be fully carried out. It also suggests how the university library can contribute to the empowerment of the deaf community.

Unseen and unheard: how the Future Generations Act is not addressing the needs of the Welsh deaf community

The Well-being of Future Generations (Wales) Act 2015 places a duty on public bodies to consider the impact of their decisions on future generations and to work towards achieving the seven well-being goals. Using doctrinal, socio-legal and autoethnographic methods, this article seeks to ascertain whether the needs of the Welsh deaf community have been considered. The Act does not explicitly mention them, and there is no evidence that public bodies have taken their needs into account when implementing the Act. The Welsh deaf community is marginalised in Welsh society, and this has been exacerbated by the closure of deaf schools and the decline of deaf clubs. The Welsh Government and the Future Generations Commissioner have a responsibility to ensure that the well-being of the Welsh deaf community is considered in the implementation of the Act and recommendations and thoughts are put forward on how they can achieve this.

Translation of a story into Chilean Sign Language: productions by deaf co-teachers

IntroductionThe incorporation of narratives in Chilean Sign Language enriches classroom practices, preserving and transmitting Deaf culture. However, that within the Chilean educational context, the narratives used for instructing Deaf students are translations from written Spanish. This study aims to describe and analyze the narrative structure and the use of Highly Iconic Structures in these translations conducted by Deaf co-teachers into Chilean Sign Language.MethodThe research adopts a qualitative approach and a descriptive case study design, involving two deaf teachers from an Inclusive Education Program in a school. The data analyzed focus on the video-recorded LSCh translations of the short story “The Greedy Squirrel”. Manual annotations were made on the corpus gloss transcription to first segment by narrative structure for description and then to identify the transfer operations.ResultsThe co-teachers make variations to the narrative structure of the translated text and incorporate specific visual-gestural elements such as the change of narrator to first person; the 45% of the translation is composed of transfer operations.DiscussionThese findings suggest that the variations in structure and the new visual-gestural information provided by the co-teachers reflect their understanding and use of sign language narrative norms for translation. The use of transfer operations enhances the storytelling experience, although it cannot be conclusively stated how and why teachers decide to use them in translation. The results are limited, as they require comparison with other corpora on translations from written Spanish to LSCh and software-assisted analysis to standardize data, which implies further research on the topic. For now, it is essential that the time allocated to the preparation of educational materials in LSCh be more extensive and better planned.

Theater performances and their accessibility in Slovakia: Insights from the Deaf community

Theater performances and their accessibility in Slovakia: Insights from the Deaf community

A qualitative reflexive thematic analysis of innovation and regulation in hearing health care

BackgroundThe hearing health sector is an example of a health sector that is experiencing a period of rapid innovation driven by digital technologies. These innovations will impact the types of interventions and services available to support the communication of deaf and hard-of-hearing individuals. This study explored the perceptions of informed participants on the topic of innovation and regulation within hearing healthcare in Australia and the United Kingdom (UK).MethodsParticipants (N = 29, Australia [n = 16], UK [n = 13]) were purposively sampled and joined one of two online workshops. Participants included adults with hearing loss and family members, hearing health professionals, academics/researchers, representatives of hearing device manufacturers, regulators and policymakers. Workshop data were analysed using reflexive thematic analysis.ResultsParticipants conceptualised the hearing health sector as a network of organisations and individuals with different roles, knowledge and interests, in a state of flux driven by innovation and regulation. Innovation and regulation were perceived as mechanisms to ensure quality and mitigate risk within a holistic approach to care. Innovations encompassed technological as well as non-technological innovations of potential benefit to consumers. Participants agreed it was essential for innovation and regulation to be congruent with societal values. Critical to ethical congruence was the involvement of consumers throughout both innovation and regulation stages, and the use of innovation and regulation to tackle stigma and reduce health disparities. Participants expressed the desire for accessible and inclusive innovation in the context of fair, transparent and trustworthy commercial practices.ConclusionsThis study explored how stakeholders within the hearing health sector understand and make sense of innovation and the role of regulation. Overall, and despite reservations relating to health care professionals’ changing roles and responsibilities, innovation and regulation were conceptualised as beneficial when situated in the context of holistic, whole-person, models of care. The results of this study will inform considerations to support the development and implementation of innovations and regulation within the hearing sector and across other health sectors influenced by technological advances.

ANALYSIS OF EXPRESSIVE AND RECEPTIVE KENYAN SIGN LANGUAGE SKILLS AMONG PRIMARY DEAF CHILDREN IN WESTERN REGION, KENYA

Research has shown that deaf children of hearing parents, who constitute 95% of the deaf community, find challenges in acquiring both sign and spoken languages. This study examined the competence of deaf children in Kenyan Sign Language. The study developed sign language tools in receptive and expressive vocabulary skills. These were sign order, conversation, placement and picture descriptive as a measure. In the majority of the assessment task, there were clear indicators of age effects, family background whether-deaf or hearing, and amount of contact with skilled signers. In the interview section, children tended to answer questions with short responses. The language was only reached at age ten. In a number of cases, simple questions were misunderstood and irrelevant answers were given. It was noted that the interaction with deaf children of deaf parents (DCDP) was very cordial as opposed to deaf children of hearing parents (DCHP). The picture description did not elicit proficient signing as was expected. The story description was not easy to understand and internalize because of the ambiguity of the children’s signing. This was because there was no indication of the protagonist. In sign order, ages ten to eleven did not seem to understand spatial grammar reliably. This test worked better in its receptive version.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0715/a.php" alt="Hit counter" /></p>

Teachers implementing Zimbabwean sign language regulatory frameworks for deaf learners in special schools

Effective implementation of sign language-based instruction is crucial for providing quality education to Deaf learners at special schools. Teachers experienced challenges to effectively implement sign language policies for the Deaf learners in Zimbabwean special schools. However, this study explores how teachers implement sign language regulatory frameworks for Deaf Learners in special schools. Drawing on qualitative data from teachers, headmasters, school inspectors, and psychologists. The findings revealed critical gaps in the in-service training and professional development opportunities available to teachers serving Deaf students. Despite teachers reporting difficulties with sign language fluency and inadequate understanding of Deaf culture and pedagogy, the study found a disconnect between teachers' expressed needs and the perspectives of district-level officials, who often did not perceive additional training as necessary. This study further highlights the heavy reliance on support from external donor organizations and NGOs to provide teachers with limited sign language workshops and resources. This overreliance on precarious external funding sources compromises the sustainability and scalability of professional development initiatives. The study's implications call for a comprehensive, government-led strategy to ensure all teachers in Deaf schools receive robust, long-term training in sign language, Deaf education methodologies, and Deaf cultural competence. Strengthening the capacity of the teaching workforce is essential for upholding the rights of Deaf learners to receive instruction in their primary language and achieving meaningful implementation of sign language policies. This study contributes vital empirical evidence to inform policy reforms and teacher training programmes to support using Zimbabwean Sign Language as a medium of instruction for the Deaf.

A deaf-centred art-science approach to community engagement with sign language technologies

Sign Language Machine Translation (SLMT) is an emerging technology that will primarily impact deaf and hard of hearing communities. Historically, these technologies have been developed by hearing scientists, without input from deaf researchers or community representatives. SignON was a three-year project exploring the application of SLMT, and using a co-creation approach which was led by the European Union of the Deaf. Here we describe an art-science method to engage deaf, hard of hearing, and hearing audiences with SLMT through theatre and performance. We also reflect on the insights, perspectives, and feedback shared by audience members.