Search Results (221)

Background/Objectives: Cochlear implant (CI) candidacy has expanded beyond traditional bilateral hearing loss (HL) to include single-sided deafness (SSD) and asymmetric hearing loss (AHL), yet baseline differences in speech recognition and patient-reported outcome measures (PROMs) between these groups—bilateral HL, SSD, and AHL—remain poorly characterized. The objective of this study was to characterize and compare preoperative speech recognition performance and PROMs between traditional bilateral HL and SSD/AHL CI candidates, and to examine associations between preoperative word recognition scores and PROMs across the full cohort. Methods: Sixty-eight adults (mean age 71.6 years, SD 7.4) undergoing preoperative CI evaluation were enrolled (31 bilateral HL, 12 SSD, and 25 AHL). Consonant–Nucleus–Consonant (CNC) word recognition and AzBio sentence recognition were assessed for both the ear-to-be-implanted (CI ear) and the contralateral ear. The following PROMs were evaluated: the Speech, Spatial and Qualities of Hearing Scale (SSQ-12); Cochlear Implant Quality of Life–35 (CIQOL-35); Patient Health Questionnaire-2 (PHQ-2); Tinnitus Handicap Inventory (THI); and the Instrumental Activities of Daily Living (IADL). Group comparisons used Mann–Whitney U tests and t-tests. CNC, SSQ-Mean, and CIQOL-Global associations were assessed using multivariable linear regression analysis. Results: Preoperative CI-ear speech recognition did not differ between the bilateral HL and SSD/AHL groups. SSD/AHL candidates had significantly higher contralateral-ear speech recognition performance, better SSQ-12 scores across all domains, and higher CIQOL-35 Global, Communication, Entertainment, and Environment scores compared to bilateral HL candidates. However, the CIQOL-35 Emotional, Listening Effort, and Social domains, PHQ-2, THI, and IADL did not differ significantly between the bilateral HL and SSD/AHL groups. Across our entire sample of candidates, CI-ear CNC scores were not significantly associated with preoperative SSQ-Mean or CIQOL-Global scores, while contralateral-ear CNC scores showed moderate, significant associations with both measures. Conclusions: Traditional bilateral and SSD/AHL CI candidates exhibit distinct preoperative PROM profiles (namely, the SSQ-12 and CIQOL-35) despite having no significant differences in CI-ear speech recognition. Contralateral-ear CNC scores—but not CI-ear scores—were significantly associated with the SSQ-Mean and CIQOL-Global, suggesting that contralateral-ear CNC scores may offer relevant insight into CI candidates’ functional hearing. These findings support population-specific counseling and highlight the complementary value of PROMs and audiometric data in CI candidacy evaluations. Full article

Across sub-Saharan Africa, Deaf congregants often remain excluded from worship, leadership, and theological formation because church practices privilege spoken communication and underinvest in sign-language access. This article develops a hearing-mediated, contextual artificial intelligence (AI) theology of disability for the African church through qualitative document analysis of the published literature on disability, Ubuntu, African ecclesiology, and emerging AI accessibility tools. This article does not report primary empirical data, but offers a conceptual synthesis requiring Deaf-led validation. Using the Contextual Disability Paradigm and Ubuntu philosophy as interpretive lenses, the article argues that AI can expand access through offline-first translation and captioning tools, but it can also weaken embodied, cross-ability relationship if technology becomes a substitute for relational labour, sign-language learning, and Deaf leadership. The article’s central contribution is the concept of the efficiency-relationality paradox: the more efficiently AI removes communicative barriers, the less incentive may remain for embodied mutuality. Because the analysis is based entirely on secondary sources and includes no Deaf-produced materials, the findings are provisional and structurally limited. The article concludes that any credible AI theology of disability in Africa must be offline-first, data-just, denominationally adaptable, and directed toward Deaf-led co-research and co-theology. Full article

Deaf people experience significant barriers to education, healthcare, employment, and information access, resulting in inequities across a myriad of contexts. To better understand these disparities, our all-deaf research team conducted semi-structured interviews with deaf and hearing (parents, caregivers, and educators) adults across Ecuador, exploring how structural, institutional, and social factors influence daily life and well-being. Participants (n = 36) described systemic exclusion from education and employment, limited access to interpreters and assistive technologies, and constrained autonomy due to insufficient family support and institutional resources. These barriers compound health risks by restricting access to care, information, and social participation. Participants’ narratives highlighted how political and economic instability, institutional neglect, and discrimination create structural vulnerabilities that extend beyond individual-level factors. Findings underscore the importance of public health interventions that address structural and communicative inequities, including inclusive education, accessible health services, and community-based support, to improve health equity and quality of life for deaf populations in Ecuador. Full article

The multilingualism found in many countries, as well as within professional groups, complicates verbal communication, as both communicating parties are required to know all the languages used. This problem is exacerbated by the fact that languages are often mixed during communication. Avatars can be used to communicate with deaf people by simulating the behavior of sign language users. This paper presents a digital sign language avatar built on a language-agnostic, multimodal animation pipeline that decouples linguistic input from animation, combining skeletal body and hand motion with facial blendshape animation as independent modalities. It also presents a methodology for assessing its quality with the participation of experts (i.e., professional sign language interpreters) and the corresponding research results. The average quality rating of the avatar interface by the experts was 5.5 on a 7-point Likert scale, indicating its potential for practical use. At the same time, the research identified opportunities to improve the naturalness of movement and the consistency of gesture transitions. Full article

Digital learning tools for American Sign Language (ASL) often lack the interactive depth necessary to engage learners effectively. This paper introduces a novel, browser-based word search game designed to facilitate ASL vocabulary familiarization through gamified interaction. The system employs a two-tier architecture consisting of a React-based frontend and a Flask-based backend. At its core, the application integrates a lightweight, skeleton-based Isolated Sign Language Recognition (ISLR) model, utilizing a Stacked Transformer-based Spatial-Temporal Attention Network to enable real-time webcam-based word entry during the configuration phase. This model, trained on the WLASL-100 dataset, achieves a Top-5 test accuracy of 88.48% with an average model inference latency of 141 ms, enabling real-time webcam input without proprietary hardware. Furthermore, we implement a constraint-satisfaction puzzle generation algorithm that achieves a 100% success rate in creating interlocked, multi-directional grids. Our results demonstrate that merging computer vision with pedagogical game mechanics provides an accessible, high-performance tool for the Deaf and Hard-of-Hearing (DHH) community, bridging the gap between static instruction and active linguistic practice. Full article

Mobile devices are widely used, yet accessibility for people with disabilities remains a critical challenge. Deaf users who rely primarily on sign language (SL) frequently encounter barriers when interacting with applications not designed for their communication needs. This study proposes a design guide for developing mobile applications tailored to sign language users. The guide was developed through the active participation of three groups: Deaf individuals, usability and user experience (UX) experts, and mobile application developers. Based on their contributions, thirteen design guidelines were defined, addressing sign language integration, visual feedback, navigation, content presentation, and interface design. The guidelines were validated through usability and UX evaluations conducted with the three participant groups. A mobile application was subsequently developed following the proposed guidelines to assess their practical applicability. The evaluation results indicate that the guide effectively supports the development of more accessible and usable mobile applications for Deaf users. Incorporating sign language-centered design principles significantly improves usability and user experience for individuals with hearing disabilities, contributing to more inclusive mobile application development. Full article

Filipino Sign Language (FSL) serves as a vital means of communication for the Deaf and hard-of-hearing in the Philippines. However, its societal use remains limited due to the scarcity of qualified interpreters and the general lack of FSL literacy among the population. Therefore, this study aims to address the gap between FSL development and automated FSL translation by employing machine learning and computer vision techniques. A model was trained using the FSL-105 dataset, which comprises video clips of gestures related to greetings and colors, and utilized MediaPipe for real-time detection of hand, face, and body landmarks. Through iterative training with transfer learning, the model’s performance improved from an initial accuracy of 80% to a final accuracy of 98.75%. The results demonstrate that the MediaPipe-based model can reliably interpret FSL gestures, positioning it as a potentially accessible assistive tool for the Deaf and hard of hearing community. This technology holds promise for applications in education, healthcare, and public service, offering new opportunities to promote the social inclusion of Filipino Deaf communities through more inclusive communication. Full article

This paper addresses the critical communication barrier experienced by deaf and hearing-impaired individuals in the Arab world through the development of an affordable, video-based Arabic Sign Language (ArSL) recognition system. Designed for broad accessibility, the system eliminates specialized hardware by leveraging standard mobile or laptop cameras. Our methodology employs Mediapipe for real-time extraction of hand, face, and pose landmarks from video streams. These anatomical features are then processed by a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN component captures spatial features, such as intricate hand shapes and body movements, within individual frames. Concurrently, BiLSTMs model long-term temporal dependencies and motion trajectories across consecutive frames. This integrated CNN-BiLSTM architecture is critical for generating a comprehensive spatiotemporal representation, enabling accurate differentiation of complex signs where meaning relies on both static gestures and dynamic transitions, thus preventing misclassification that CNN-only or RNN-only models would incur. Rigorously evaluated on the author-created JUST-SL dataset and the publicly available KArSL dataset, the system achieved 96% overall accuracy for JUST-SL and an impressive 99% for KArSL. These results demonstrate the system’s superior accuracy compared to previous research, particularly for recognizing full Arabic words, thereby significantly enhancing communication accessibility for the deaf and hearing-impaired community. Full article

This qualitative study investigates the challenges teachers face when teaching English as a foreign language (EFL) to deaf (in this article, deaf (lower case) refers to the audiological condition of hearing loss, whereas Deaf (capitalized) is used to denote individuals who identify as members of the Deaf community and share a common sign language and distinct cultural values) and hard-of-hearing (DHH) students in German schools for the Deaf. The study is situated within a structural–theoretical professionalization framework, which focuses on the relationship between institutional conditions, teacher education structures, and professional action. Semi-structured interviews were conducted with 16 teachers of DHH students and the data were examined using qualitative content analysis. The findings reveal five central areas of challenge: (1) heterogeneity of the student body; (2) limited time (for preparing and adapting materials); (3) restricted subject-matter and sign-language competence, including missing links between EFL didactics and Deaf education in teacher training; (4) uncertainties surrounding the language design of EFL instruction, particularly the role of American Sign Language (ASL), German Sign Language (DGS), and written English; and (5) the lack of consistent, accessible exam formats and standards. Teachers report substantial insecurity due to the absence of coherent concepts, policy frameworks, and specialized training pathways, which fosters divergent classroom practices and tensions within teaching staff. The results highlight an urgent need for systematic integration of Deaf education, sign language training, and EFL pedagogy in teacher education, as well as for evidence-based guidelines on language classroom practice and assessment for DHH learners. Full article

We present a Tagalog lip-reading system designed to enhance communication accessibility for individuals with hearing impairments. Existing lip-reading models focus on English and other major languages and cannot recognize Tagalog visual speech patterns. To address this gap, we implemented 3D Convolutional Neural Network combined with Bidirectional Long Short-Term Memory network, supported by a custom Tagalog dataset of common words. This architecture achieved an average character error rate of 10.09%, word error rate of 24.08%, and overall word accuracy of 76.27%, demonstrating promising recognition accuracy for Tagalog lip movements. By introducing the Tagalog-specific lip-reading framework, the potential of deep learning-based visual speech recognition was validated to support inclusive technologies, with applications in daily communication, education, and assistive tools for the Filipino deaf community. Full article

Persistent communication barriers for the deaf and hard-of-hearing community in the Philippines are addressed in this study by developing a Filipino Sign Language Recognition (SLR) system. The system focuses on transactional signs commonly used in commercial environments such as markets and public facilities, thereby filling a gap left by existing SLR models. A vision-based approach was adopted, employing MediaPipe for landmark detection and Gated Recurrent Units for translating signs into text. To train the model, a custom dataset comprising 1065 video samples of 26 transactional signs was created, accounting for subtle variations in individual signing styles. The complete system was implemented on a Raspberry Pi 5 equipped with a webcam and touchscreen display. When evaluated on unseen data, the system achieved a recognition accuracy of 87%, demonstrating its potential for real-world applications in supporting commercial interactions for deaf and hard-of-hearing individuals. Full article

Background/Objectives: Approximately half of US children who meet traditional cochlear implant (CI) candidacy criteria receive an implant. As candidacy expands to include a broader range of hearing configurations, identifying factors that influence referral completion and CI uptake is increasingly important. This study examined the predictors of CI evaluation completion and surgery among children referred for CI assessment, including both traditional and non-traditional candidates. Methods: The medical records of pediatric patients referred for an initial CI evaluation from 2018 through 2024 were reviewed. Referral outcomes were categorized as evaluation not completed, candidate who declined surgery, or candidate who proceeded with surgery. Two binomial logistic regression models assessed the demographic, audiologic, and contextual predictors of CI evaluation completion and CI uptake, including age at referral, candidate type, insurance, referral year, communication mode, race/ethnicity, unaided thresholds, rurality, and county-level social health determinants. Results: The completion of the CI evaluation was significantly influenced by race/ethnicity, candidate type, referral year, and age. Mixed-race children demonstrated higher completion rates than White children. Completion was lower among children with single-sided deafness (SSD), children referred in 2022, and older children. Among children determined to be candidates, 69% proceeded with surgery. CI uptake showed similar patterns, with lower rates among Hispanic children, children with residual hearing or SSD, children referred in 2022, and older children. Conclusions: CI uptake at this center exceeded national averages but was associated with race/ethnicity, candidate type, age, and year of referral. Targeted counseling and outreach may improve timely referral and informed decision-making. Full article

Visual speech recognition systems encounter significant challenges in multilingual nations such as the Philippines, where numerous regional languages, including Cebuano and Ilocano, feature distinct phonetic-visual characteristics. Deep learning models such as the Lip Reading Network and the Lightweight Crowd Segmentation Network have demonstrated strong performance with 3D Convolutional Neural Networks (CNNs). However, their substantial computational requirements restrict deployment on portable edge devices. We introduce a more efficient alternative that integrates a 2D Residual Network 50 architecture with a Long Short-Term Memory network and Connectionist Temporal Classification for lip-reading classification of Philippine regional languages. The proposed model is deployed on the Jetson Orin Nano, a high-performance edge device optimized for real-time inference through Compute Unified Device Architecture acceleration. Using a dataset of 2000 annotated videos encompassing 10 lexicons each for Cebuano and Ilocano, the model’s effectiveness was evaluated. Results achieved a regional language classification accuracy of 90%, with lexicon-level accuracies of 74% for Cebuano and 66% for Ilocano. This work represents a step toward developing accessible and scalable communication aids for deaf communities in linguistically diverse environments, leveraging transfer learning on pretrained models. Full article

A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype electronic glove that translates Mexican Sign Language (LSM) signs into Spanish text using machine learning. Eight participants (four deaf and four hearing with LSM proficiency) completed four sessions involving 12 signs; three sessions (S1–S3) were used for model development and one session (T) was held out for evaluation. Models were trained on S1–S3 and tested on T using a session-level split without window mixing across sessions; therefore, results represent a speaker-dependent, inter-session pilot assessment rather than a speaker-independent generalization test. The glove integrates flex sensors and an inertial measurement unit IMU MPU6050 connected to an ESP32-C3 SuperMini microcontroller. These components were selected due to their low cost, availability, and ease of integration, making them suitable for the development of accessible wearable assistive technologies. Under this protocol, the system achieved a window-level overall test accuracy of 97.0% (95% CI computed at the window level: 96.00–97.00), with higher performance for the dynamic subset (98.0%) than for the static subset (95.0%), and an algorithmic decision delay of 1.2 s. Usability and acceptance were evaluated using the System Usability Scale (SUS) and a Technology Acceptance Model (TAM)-based questionnaire. The mean SUS score was 50.6 ± 1.8 (marginal usability), while participants reported positive perceptions across TAM constructs. Overall, findings demonstrate technical feasibility under controlled inter-session conditions and provide a foundation for iterative user-centered refinement, followed by strict speaker-independent validation and classroom deployment studies in future work. Full article

Deaf and Hard-of-Hearing (DHH) students face persistent barriers in geometry education due to instructional approaches that inadequately support visual communication and embodied learning. This study examined DHH students’ experiences with GeoMETriA, a metaverse-based geometry learning platform integrating sign language instruction, three-dimensional visualization, and avatar-mediated interaction. Guided by the Cognitive Affective Model of Immersive Learning (CAMIL), a multi-phase qualitative design was employed, including pre-workshop interviews with four special education teachers and post-workshop focus group discussions with seven DHH secondary students following a four-session learning workshop. The findings indicate that gamified activities and peer collaboration enhanced interest and sustained engagement, while avatar customization supported embodiment and a sense of presence. Students described progression from initial uncertainty to greater confidence through practice and scaffolded support. However, cognitive and usability challenges emerged, particularly concerning sign language video pacing, navigation complexity, and limited instructional scaffolding. The study contributes theoretically by extending CAMIL-informed interpretations to sign-supported metaverse learning, empirically by documenting how engagement, embodiment, and self-efficacy develop during immersive geometry learning, and practically by offering design implications including adjustable sign language delivery, structured scaffolding, and culturally responsive avatar options. These findings suggest that metaverse-based platforms hold promise for supporting DHH learners when accessibility and learner-centered principles are embedded as foundational design considerations. Full article