Search Results (148)

The acquisition of a person’s first language is one of their greatest accomplishments. Nevertheless, being fluent in sign language presents challenges for many deaf students who rely on it for communication. Effective communication is essential for both personal and professional interactions and is critical for community engagement. However, the lack of a mutually understood language can be a significant barrier. Estimates indicate that a large portion of New Zealand’s disability population is deaf, with an educational approach predominantly focused on oralism, emphasizing spoken language. This makes it essential to bridge the communication gap between the general public and individuals with speech difficulties. The aim of this project is to develop an application that systematically cycles through each letter and number in New Zealand Sign Language (NZSL), assessing the user’s proficiency. This research investigates various machine learning methods for hand gesture recognition, with a focus on landmark detection. In computer vision, identifying specific points on an object—such as distinct hand landmarks—is a standard approach for feature extraction. Evaluation of this system has been performed using machine learning techniques, including Random Forest (RF) Classifier, k-Nearest Neighbours (KNN), AdaBoost (AB), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), and Logistic Regression (LR). The dataset used for model training and testing consists of approximately 100,000 hand gesture expressions, formatted into a CSV dataset for model training. Full article

In an increasingly globalized world, learning foreign languages (FLs) is essential, particularly in education. Multilingualism is critical due to the multicultural and interconnected nature of societies, yet early third language acquisition (TLA) is not widely adopted in schools. This study investigates how the simultaneous learning of Spanish first language (L1), a second language (L2), and a third language (L3) impacts oral language (OL) development in L1 and whether prior L2 knowledge aids L3 acquisition. The study involved bilingual (L1 + L2) and trilingual (L1 + L2 + L3) learners. Data were collected using the Navarre Oral Language Test-Revised, which evaluates phonological, morphological–syntactic, lexical–semantic, and pragmatic competencies in oral communication. Findings revealed that trilingual learners showed better OL development in L1 compared to bilingual learners. Additionally, prior L2 knowledge facilitated L3 learning, highlighting the benefits of early trilingual education. The study demonstrates that early trilingual learning positively impacts OL development in L1. These results contribute significantly to research on TLA and the advancement of multilingual education. Full article

Parenting stress significantly affects caregivers of deaf and hard-of-hearing (DHH) children, influenced by unique challenges and stressors. Background/Objectives: This study aims to develop the Chinese Family Stress Scale (CFSS) and to identify the stressors and contributing factors to elevated stress levels. Methods: The study involved 257 caregivers of DHH children aged 0–12 years old. The CFSS was used to assess parenting stress in caregivers of DHH children, with its reliability and validity evaluated. Factors such as speech intelligibility, oral language use, self-compassion, and social support were examined for their impact on parenting stress. Results: Key stressors included financial issues, discipline, education concerns, medical care, and safety. Elevated parenting stress was significantly associated with poor speech intelligibility of the child, inadequate oral language use, negative aspects of self-compassion, and insufficient social support. The CFSS showed good reliability and validity in measuring parenting stress among caregivers of DHH children. Conclusions: The CFSS is an effective tool for assessing parenting stress in caregivers of DHH children. Interventions to reduce parenting stress can focus on improving children’s communication skills, enhancing caregiver self-compassion, and bolstering social support networks. Full article

Developmental Language Disorder (DLD) and Dyslexia frequently co-occur. Many studies suggest that children with Dyslexia may also struggle with oral language skills, while those with DLD may also face challenges in word decoding. Both groups of children typically exhibit deficits in phonological processing [phonological awareness, rapid automatized naming (RAN), verbal short-term memory (VSTM)] and verbal working memory (VWM). Despite the increasing number of comparative studies, few have systematically examined these overlaps in children at the early stages of literacy acquisition, and few studies have systematically assessed all oral language subsystems. This study investigates: (a) differences and similarities among children with DLD, children at Risk of Dyslexia (RfD), and typically developing children (TD) in phonological processing (phonological awareness—implicit and explicit—VSTM, RAN), VWM, oral language, and word decoding; (b) patterns of performance across groups; and (c) correlations between phonological processing and VWM skills with oral language and word decoding. The participants were 45 first graders (Mage = 6.8), equally divided into three groups (DLD, RfD, and TD children). Both special groups (DLD, RfD) performed significantly worse than the TD group across nearly all measurements. DLD children showed pronounced oral language and VSTM deficits, while RfD children primarily struggled with decoding and explicit phonological awareness. Group-specific correlations emerged. The findings are discussed in light of the theoretical models of the relationship between DLD and Dyslexia taking into account the specific developmental stage. Full article

This paper explores the rise in voice-based social media as a pivotal transformation in digital communication, situated within the broader era of chatbots and voice AI. Platforms such as Clubhouse, X Spaces, Discord and similar ones foreground vocal interaction, reshaping norms of participation, identity construction, and platform governance. This shift from text-centered communication to hybrid digital orality presents new sociological and methodological challenges, calling for the development of voice-centered analytical approaches. In response, the paper introduces a multidimensional methodological framework for analyzing voice-based social media platforms in the context of surveillance capitalism and AI-driven conversational technologies. We propose a high-level reference architecture machine learning for social science pipeline that integrates digital methods techniques, automatic speech recognition (ASR) models, and natural language processing (NLP) models within a reflexive and ethically grounded framework. To illustrate its potential, we outline possible stages of a PoC (proof of concept) audio analysis machine learning pipeline, demonstrated through a conceptual use case involving the collection, ingestion, and analysis of X Spaces. While not a comprehensive empirical study, this pipeline proposal highlights technical and ethical challenges in voice analysis. By situating the voice as a central axis of online sociality and examining it in relation to AI-driven conversational technologies, within an era of post-orality, the study contributes to ongoing debates on surveillance capitalism, platform affordances, and the evolving dynamics of digital interaction. In this rapidly evolving landscape, we urgently need a robust vocal methodology to ensure that voice is not just processed but understood. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. Full article

The understanding of hidden emotions—situations in which individuals deliberately express an emotion different from what they genuinely feel—is a key skill in theory of mind (ToM) development. This ability allows children to reason about discrepancies between internal emotional states and external expressions and is closely tied to linguistic development, particularly vocabulary related to mental states, which supports complex emotional reasoning. Children who are deaf or hard of hearing (DHH), especially those born to hearing non-signing families and raised in oral language environments, may face challenges in early language exposure. This can impact the development of social and emotional skills, including the ability to understand hidden emotions. This study compares the understanding of hidden emotions in hearing children (n = 59) and DHH children (n = 44) aged 7–12 years. All children were educated in spoken language environments; none of the DHH participants had native exposure to sign language. Participants completed a hidden emotions task involving illustrated stories where a character showed a certain emotion in front of two observers, only one of whom was aware of the character’s true emotional state. The task assessed children’s understanding of the character’s emotional state as well as their ability to reason about the impact of hiding emotions on the beliefs of the observers. The results showed that the hearing children outperformed their DHH peers in understanding hidden emotions. This difference was not attributed to hearing status per se but to language use. Specifically, children’s spontaneous use of cognitive verbs (e.g., think or know) in their explanations predicted task performance across the groups, emphasizing the role of mental state language in emotional reasoning. These findings underscore the importance of early and accessible language exposure in supporting the emotional and social cognitive development of DHH children. Full article

The Mutanchis, known by their derogatory exonymic term ‘Lepcha’, are autochthonous to Sikkim, India. The name ‘Mutanchi’ derives from the phrase ‘Mutanchi Rumkup Rongkup’, eliciting the response ‘Achulay’, meaning ‘Beloved children of It-bu-mu, who have come from the snowy peaks’. The nomenclature prompts an ontological understanding rooted in the community’s eco-geographical context. Despite possessing a well-developed script categorised within the Tibeto-Burman language family, the Mutanchis remain a largely oral community. Their diminishing, scarcely documented repository of Mutanchi clan narratives underscores this orality. As a Mutanchi, I recognise these narratives as a medium for expressing Indigenous value systems upheld by my community and specific villages. Mutanchi clan narratives embody spiritual and cultural significance, yet their fantastic rationale reveals complex epistemological tensions. Ideally, each Mutanchi clan reveres a chyu (peak), lhep (cave), and doh (lake), which are propitiated annually and on specific occasions. The transmigration of an apil (soul) is tied to these three sacred spatial geographies, unique to each clan. Additionally, clan etiological explanations, situated within natural or supernatural habitats, manifest beliefs, values, and norms rooted in a deep ecology. This article presents an ecosophical study of selected Mutanchi clan narratives from Dzongu, North Sikkim—a region that partially lies within the UNESCO Khangchendzonga Man-Biosphere Reserve. Conducted in close consultation with clan members and in adherence to the ethical protocols, this study examines clans in Dzongu governed by Indigenous knowledge systems embedded in their narratives, highlighting biocentric perspectives that shape Mutanchi lifeways. Full article

Background/Objectives: Tongue thrust (TT) occurs when abnormal tongue movements cause anterior tongue placement with pressure and contact against or between the teeth, potentially affecting the oral phase of swallowing, impacting eating, breathing and speaking. There is limited literature on the diagnostic and treatment approaches for TT, as well as involvement of health practitioners in its management. This study aims to examine the current knowledge and practices related to TT diagnosis and treatment among health professionals in Australia. Methods: A two-phase explanatory sequential mixed methods approach was adopted, comprising an online survey that collected participants’ demographic information and details on assessment, diagnosis, management, referral practices, and relevant experience and training. Phase one involved 47 health professionals from various disciplines in Australia who completed an online survey in its entirety. Phase two included in-depth interviews with seven speech-language pathologists (SLPs) to gain further insights into their experiences in managing TT. Survey data were analysed descriptively, and interview data was analysed thematically. Results: Most participants diagnosed TT using clinical assessments, such as general observation and oral motor examinations. Treatment approaches commonly included orofacial myofunctional therapy and the use of myofunctional devices. Interviews with SLPs identified four key themes: tongue thrust as a symptom rather than a diagnosis, facilitators to effective treatment, multidisciplinary approaches to management, and training and education gaps in clinical practice. Conclusions: This study provides valuable insights into how TT is identified, assessed, diagnosed, and managed by health professionals in Australia. It highlights the perspectives of SLPs on treatment approaches, as well as their views on the availability and adequacy of training and education in this field. The findings suggest the need for a broader understanding of TT management, emphasising the importance of multidisciplinary collaboration and professional development. These insights are globally relevant, as they stress the shared challenges and the value of international collaboration in improving TT diagnosis and treatment practices. Full article

Multiple studies have investigated the impact of the tumor immune microenvironment (TIME) on oral squamous cell carcinoma (OSCC), with most focusing on three key cellular components: lymphocytes, macrophages, and neutrophils, as well as the molecular mechanisms underlying inflammation-mediated OSCC invasion. Although the specific roles of each cell type vary depending on their subtypes and the characteristics of OSCC, several consistent patterns have been identified. TIME plays a critical role at every stage of OSCC progression, from tumor initiation and growth to invasion and metastasis. Understanding the communication signals–the language–between tumor cells and the TIME, encoded through various proteins secreted by immune cells, is essential for controlling tumor progression and developing effective treatments for OSCC. This review provides an overview of how TIME influences the progression of the Oral Potentially Malignant Disorders (OPMDs) to OSCC as well as OSCC’s invasion, focusing on the contributions of various immune cells within the TIME. Additionally, we discuss recent advances in immunotherapy for OSCC, highlighting strategies to enhance immune responses and improve treatment outcomes. Full article

The rapid development of science and technology enables technological innovations to change the way of English oral learning. Based on the use of a large language model (LLM), we developed a novel dynamic evaluation system for oral English, LLM-DAELSL, which combines daily oral habits and a textbook outline. The model integrates commonly used vocabulary from everyday social speech and authoritative prior knowledge, such as oral language textbooks. It also combines traditional large-scale semantic models with probabilistic algorithms to serve as an oral assessment tool for undergraduate students majoring in English-related fields in universities. The model provides corrective feedback to effectively enhance the proficiency of English learners through guided training at any time and place. The technological principle of the model involves inputting prior template knowledge into the language model for reverse guidance and utilizing the textbooks provided by China’s Ministry of Education. The model facilitates the practice and evaluation of pronunciation, grammar, vocabulary, and fluency. The six-month tracking results showed that the oral proficiency of the system learners was significantly improved in the four aspects, which provides a reference for other language learning method developments. Full article

The research on teaching scaffolding for preschool children’s oral language development (OLD) has become an important topic in the academic world. However, there remains a lack of evidence-based research on the integration of scaffolding strategies integrated into creative art contexts to support children’s creative expression and language production. In this study, a qualitative case study was conducted to analyze the non-participatory observation and artwork analysis of five-year-old children’s drawing activities in a kindergarten in China based on socio-cultural and scaffolding theories. Three types of core scaffolding strategies were summarized. The findings reveal that the three strategies work together dynamically within the children’s Zone of Proximal Development (ZPD): (1) the visual prompt strategy enriches the vocabulary diversity of metaphors, adjectives, and ordinal words; (2) dialogic narrative co-construction effectively improves narrative coherence across exposition, rising action, climax, and resolution; and (3) emotional engagement strategies foster a safe expressive environment, promoting the integration of affective vocabulary with intrinsic motivation. Accordingly, a three-dimensional integrated “visual-linguistic-emotional” scaffolding model was constructed, emphasizing the practical guidelines of simultaneous scaffolding and gradual scaffolding withdrawal during the warm-up, creation, and sharing sessions of the drawing activity. This study expands the application of scaffolding theory in unstructured art contexts, and provides a systematic practical framework for the design of cross-contextual language support strategies and teacher training in preschool education. Full article

Oral and craniomaxillofacial bone deformities necessitate treatment through osteotomy. Robot-assisted osteotomy appears promising in oral and craniomaxillofacial surgery, but it lacks sufficient intelligence and comprehensive integration of navigation tracking with surgical planning. This study aims to develop an intelligent surgical robot, based on the large language model ChatGPT-4, to enable autonomous planning for oral and craniomaxillofacial osteotomies. An autonomous surgical planning system driven by ChatGPT-4 was developed. Surgical plans were autonomously generated based on expert-defined prompts and surgical objectives. A deep learning framework was employed to match navigation-generated visual data with textual planning outputs. The generated plans were subsequently converted into executable instructions for robotic surgery. System precision, execution accuracy, and usability were experimentally validated through common osteotomies. An anonymous Likert scale assessed operational efficiency. The proposed system achieved a trajectory planning accuracy of 0.24 mm and an average robotic execution accuracy of 1.46 mm. The completion rates for two representative procedures, Le Fort I osteotomy and genioplasty, were 87% and 92%, respectively. Survey results confirmed process feasibility. The integration of a large language model with surgical robot advances intelligent, precise, and safe oral and craniomaxillofacial osteotomy procedures. Full article

South Africa continues to experience a high HIV prevalence, necessitating innovative prevention strategies aligned with the UNAIDS 95-95-95 targets. Long-acting injectable pre-exposure prophylaxis (PrEP), such as cabotegravir (CAB-LA), offers a promising alternative to daily oral regimens. However, the perspectives of primary healthcare workers (PHCWs)—key implementers of this intervention—remain underexplored. This scoping review aims to systematically map existing literature on PHCWs’ knowledge, awareness, perceptions, barriers, facilitators, and implementation experiences related to injectable PrEP within the South African healthcare context. The review will follow the Arksey and O’Malley framework, enhanced by Levac et al., and will be reported following PRISMA-ScR guidelines. A comprehensive search will be conducted across PubMed, Scopus, CINAHL (EBSCOhost), Web of Science, and Google Scholar, without language or date restrictions. The search strategy will employ both controlled vocabulary (e.g., MeSH and CINAHL Subject Headings) and free-text terms. Studies meeting the inclusion criteria will be managed using EndNote X20 and appraised using the Mixed Methods Appraisal Tool (MMAT) 2018 version. Data will be synthesized thematically and presented narratively and in tabular form. By consolidating PHCWs’ perspectives, this review will identify implementation challenges, training needs, and systemic barriers, informing the development of context-specific strategies for PrEP rollout. The findings are expected to support the design of effective, culturally relevant educational interventions and guide policymakers in strengthening HIV prevention efforts in high-burden settings. Full article

This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI or a human partner. To evaluate the impact of interaction mode on oral proficiency, participants completed a story retelling task, assessed using complexity, accuracy, and fluency (CAF) metrics. Each participant engaged in both partner conditions, with group order counterbalanced. The results from the retelling task indicated modest improvements in fluency and complexity, particularly following interaction with the AI partner. Accuracy scores remained largely stable across conditions. Post-task reflections revealed that learners perceived AI partners as less intimidating, facilitating more relaxed language production, though concerns were noted regarding limited responsiveness. Qualitative analysis of the gameplay transcripts further revealed contrasting interactional patterns: AI partners elicited more structured interactions whereas human partners prompted more spontaneous and variable interactions. These findings suggest that AI-mediated gameplay can enhance specific dimensions of spoken language development and may serve as a complementary resource alongside human interaction. Full article