Sign Language

The study of Personal Deixis is one of the important aspects of language in the field of pragmatics. One of its most obvious features is dependence on subjectivity and context. Because personal deixis are clear examples of the cultural and social characteristics of a language; The classification, interpretation and analysis of Personal Deixis and their role in literary texts such as short story, is one of the most important aims of this research. In this research, we find the descriptive-statistical method in analyzing the frequency and role of personal deixis in the short story "Dash Acol". The results obtained in this study shows that personal deixis play an important role in conveying meaning to the reader. The most common of them are in the form of personal pronouns with a frequency of (% 75.09) percent, the demonstrative pronoun, at a rate of (% 6.7), special nouns, with a percentage (% 4.91) and nicknames, with (% 12.32) in next rank. The lowest frequency takes kinship terms (% 4.32).

This paper presents the SAILSE Project (Sistema Avanzado de Información en Lengua de Signos Española -Spanish Sign Language Advanced Information System). This project aims to develop an interactive system for facilitating the communication between a hearing and a deaf person. The first step has been the linguistic study, including a sentence collection, its translation into LSE (Lengua de Signos Española -Spanish Sign Language), and sign generation. After this analysis, the paper describes the interactive system that integrates an avatar to represent the signs, a text to speech converter and several translation technologies. Finally, this paper presents the set up carried out with deaf people and the main conclusions extracted from it.

India being the second most populated country in the world with over a billion population and over a million hearing impaired and diabetes disease patients, a translation system which can translate a given input into sign languages can be used to disseminate information to the million hearing impaired patients. Such people find it difficult to access information in common places like hospitals and railway stations. A translation system which can convert English into Indian Sign Languages can be developed to help such people. Machine Translation (MT) is an innovative paradigm that promotes the advancement of science and technology to built smart environments. It advocates an invisible technological support layer of information processing to improve the quality of life. In this paper we discuss. Present study contains possibilities of integrating concept related to natural language processing (NLP) certain aspects of integrating English languages to sign language.

This paper, submitted as a graduate milestone assignment for Nexford University's AIA 6600 course, presents a memorandum addressed to the executive leadership of the Midwest Cardiology Institute, proposing the use of Artificial Intelligence to maximise revenue through the early detection of heart diseases.
In this submission, I explained how AI can transform cardiology practice by enhancing diagnostic accuracy, optimising resource allocation, and shifting care models toward value-based reimbursement. Two core AI technologies are proposed: Convolutional Neural Networks (CNNs) implemented via TensorFlow for cardiac image recognition (echocardiograms, MRIs, CT scans), and Long Short-Term Memory (LSTM) Recurrent Neural Networks for real-time ECG anomaly detection. Detailed technical implementation steps are provided for both models, including data pre-processing, model architecture, training pipelines, and clinical deployment strategies. Projected outcomes include a diagnostic accuracy of 92–95% for cardiac imaging and a 35–45% improvement in arrhythmia detection sensitivity, with a combined estimated annual revenue uplift of $420K–$560K. This paper concludes with recommendations to invest in AI infrastructure, collaborate with data science experts, and establish continuous evaluation processes to ensure clinical compliance and long-term impact.

Sign language is developed to bridge the communication gap between individuals with and without hearing impairment or speech difficulties. Individuals with hearing and speech impairment typically rely on hand signs as a means of expressing themselves. However, people, in general, may not have sufficient knowledge of sign language, thus a sign language recognition system on an embedded device is most needed. Literature related to such systems on embedded devices is scarce as these recognition tasks are very complex and computationally expensive. The limited resources of embedded devices cannot execute complex algorithms like Convolutional Neural Network (CNN) properly. Therefore, in this paper, we propose a novel deep learning architecture based on default Xception architecture, named Quantized Modified Xception (QMX) to reduce the model's size and enhance the computational speed without compromising model accuracy. Moreover, the proposed QMX model is highly optimized due to the weight compression of model quantization. As a result, the footprint of the proposed QMX model is 11 times smaller than the Modified Xception (MX) model. To train the model, BDSL 49 dataset is utilized which includes approximately 14,700 images divided into 49 classes. The proposed QMX model achieves an overall F1 accuracy of 98%. In addition, a comprehensive analysis among QMX, Modified Xception Tiny (MXT), MX, and the default Xception model is provided in this research. Finally, the model has been implemented on Raspberry Pi 4 and a detailed evaluation of its performance has been conducted, including a comparison with existing state-of-the-art approaches in this domain. The results demonstrate that the proposed QMX model outperforms the prior work in terms of performance.

Il progetto della collana "Il grande albero" nasce dall'idea di rendere accessibili, sia nella forma che nei contenuti, gli argomenti che riguardano tutti noi: la scuola, la diversità, la società, l'integrazione. I cambiamenti ai quali sono sottoposti tutti gli ambiti inerenti al sociale, meritano approfondimenti che non siano solo scientifici, o solo narrativi, o solo visti in chiave poetica, ma necessitano di un luogo che conceda spazio a tutte le esigenze. Questo perché la società, nei suoi multiformi aspetti, non può essere vista da una sola prospettiva: non solo scientifica, pur essendo essa assolutamente necessaria, poiché si rischierebbe di spersonalizzarla e spogliarla dei suoi aspetti umani. Non solamente poetica o narrativa, poiché si perderebbe di vista l'esigenza di analizzarne gli aspetti e le criticità, riducendola a vuote drammatizzazioni e luoghi comuni. Per questo la collana "Il grande albero" nasce con l'intento di dare spazio a testi che trattino dal punto di vista scientifico le difficoltà che quotidianamente si presentano nella scuola: i D.S.A., le difficoltà comunicative, il bilinguismo, l'integrazione, l'inclusione delle persone disabili. Ma nasce anche con l'intento di dare spazio alla narrativa, a storie che offrano spaccati di società che diano la possibilità di riflettere e che contemporaneamente tocchino la sfera emotiva suscitando interesse e partecipazione.

There is a dearth of qualitative research concerning deaf people’s
experiences of participating in the Irish Sign Language (ISL) recog-
nition movement, and this limits our ability to understand the op-
portunities and constraints they encountered as they negotiated their
way toward the passing of the ISL Act 2017. While ISL is unique to
Ireland, it is also of crucial importance to deaf people’s identity and
the quality of their social and educational life. The aim of this quali-
tative study is to reveal the primary findings derived from interviews
with nine deaf activists who participated in this study. Framed within
the social movement theory and the Freirean concept of critical
consciousness, the study illuminates the background on how and
why deaf activists managed to overcome some significant challenges
during the ISL movement. The findings show that deaf activists suc-
cessfully negotiated their way through the political and legal system
to achieve their goal. Discussions and conclusions drawn from the
findings suggest that deaf people have the potential to be both agents
of social change and effective social movement activists. Finally, the
article seeks to be added to the canon of literature on sign language
studies and the emerging research on sign language recognition.

The meaning of a scholarly intervention is not determined solely by what it says, but by what it enables within a field. Where visibility and influence are uneven, neutrality cannot produce neutral outcomes. Meaning does not stay inside a text. It moves—through interpretation, through uptake, through what is amplified, and through what is masked—or ignored. What appears stable when written does not remain fully stable when encountered. This paper, not intended for peer review publication offers a short essay examining the dynamics a modernist-postmodernist theoretical impasse and a potential alternative--metamodernism

Penelitian ini bertujuan mengidentifikasi strategi komunikasi orang tua dalam menumbuhkan rasa percaya diri anak usia dini melalui kajian literatur. Hasil analisis menunjukkan bahwa terdapat tiga bentuk komunikasi utama yang digunakan orang tua, yaitu verbal, nonverbal, dan asertif. Komunikasi verbal dilakukan melalui pemberian pujian dan kata-kata motivasi yang membantu anak merasa dihargai atas usaha yang telah dilakukan. Komunikasi nonverbal berupa senyuman, pelukan, dan sentuhan lembut berperan dalam memberikan rasa aman dan kenyamanan emosional. Komunikasi asertif digunakan orang tua dengan menetapkan aturan secara jelas serta memberi ruang bagi anak untuk memilih, sehingga mendukung perkembangan kemandirian dan kepercayaan diri. Faktor pendukung strategi ini meliputi kehadiran orang tua, pola komunikasi terbuka, dan lingkungan keluarga yang harmonis. Sedangkan faktor penghambat berasal dari kesibukan orang tua, pola komunikasi otoriter, serta lingkungan sekitar yang kurang mendukung perkembangan anak.

People interact their environment by communicating, but some limitation may exist such as hearing disability that occurs to deaf people. Usually, they communicate by using sign language. Unfortunately, there is various sign language in the world. This research is evaluating the usability of smartphone for communication board that can be used by deaf people to communicate to others, especially the people who don’t understand the sign language. Usability testing is to measure the user performance for mobile applications. The five criteria for the usability, according to Nielsen are learnability, efficiency, memorability, errors, and satisfaction. The study obtained the result of 88.36% of usability testing.

Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven.

Sign language constitutes the primary mode of communication for an estimated 466 million deaf and hard-ofhearing individuals worldwide, yet the overwhelming majority of hearing individuals cannot understand it. Existing sign language recognition and translation systems address only a single sign language in isolation, are limited to static gestures or a small lexicon, lack real-time bidirectional translation, and fail to support multiple regional sign languages simultaneously. This paper proposes UnifySign: a unified, AI-powered, real-time framework for multi-sign language recognition and translation, supporting Indian Sign Language (ISL), American Sign Language (ASL), Pakistan Sign Language (PSL), and Indo-Pakistani Sign Language (IPSL). The system integrates YOLOv8 for bounding-box detection, MediaPipe Holistic for 21-point hand landmark extraction, Convolutional Neural Networks (CNN) for static sign classification, Long Short-Term Memory (LSTM) networks for dynamic gesture sequence recognition, and a Large Language Model (Google Flan-T5) for grammatically coherent sentence generation. A bidirectional translation pipeline supports both sign-to-text and speech/text-to-sign directions. Offline mobile deployment (Flutter + TFLite) and a web-based interface (Flask) ensure accessibility across device types. Experimental evaluation on INCLUDE, MS-ASL, WLASL, and a custom PSL/IPSL corpus demonstrates accuracy exceeding 97% for ISL/ASL static recognition, 95% for dynamic gestures, and 91% for sentence-level translation. The proposed system surpasses all ten reviewed state-of-the-art systems in coverage, accuracy, and real-world deployability.

In the past few years the development in the field of Gesture Recognition and the tools, devices, models and algorithms are into existence. Regardless of various innovations in the above technologies humans are still comfortable with showing gestures according to the plain hand. In this paper we focus on a system that runs on a mobile computing device which provides us the technology for automated translation of the Indian Sign Language system into Speech in the English Language and thus provides a duplex communication between the vocally impaired people and the common people of the society. This system can be very effective and can be used in the near future for communication between the people who do not understand the Sign language since it works on the principle of Gesture Recognition. The system uses an intrinsic mobile camera for gesture recognition and acquisition; gesture acquired is processed with the help of various algorithms. The system is able to recognize one handed sign representation of the standard alphabets (A-Z) and numeric values (0-9). The output of this system is very efficient, consistent and of high approximation of gesture processing and speech analysis.

I extend my sincere thanks to Pr Afef Abdelkrim, Reviewer, for accepting to evaluate my thesis. Her thorough review and insightful feedback have contributed greatly to enhancing the credibility and quality of my work. I express my deepest gratitude to Pr Afef Elloumi Oueslati, Professor at the National School of Engineers of Carthage (ENICAR), for her exceptional guidance throughout my research journey. Her unwavering support, valuable advice, availability, and trust in my decisions have been invaluable to the success of this project. Please accept my heartfelt appreciation and profound respect. I am also deeply thankful to Dr Fériel Ben Nasr for her collaboration and indispensable assistance during the course of my work. Her expertise and dedication have significantly enriched this research. Lastly, I am deeply grateful to my family, friends, and colleagues, whose moral support and encouragement have been a constant source of strength throughout this journey.

Sign language is a medium of communication for people with hearing and speaking impairment. It uses gestures to convey messages. The proposed system focuses on using sign language in search engines and helping specially-abled people get the information they are looking for. Here, we are using Marathi sign language. Translation systems for Indian sign languages are not much simple and popular as American sign language. Marathi language consists of words with individual letters formed of two letter = Swara + Vyanjan (Mulakshar). Every Vyanjan or Swara individually has a unique sign which can be represented as image or video with still frames. Any letter formed of both Swara and Vyanjan is represented with hand gesture signing the Vyanjan as above and with movement of signed gesture in shape of Swara in Devnagari script. Such letters are represented with videos containing motion and frames in particular sequence. Further the predicted term can be searched on google using the sign searc...

escrito dessa língua e interferências estruturais da Libras. Assim, conclui-se que os significados dos textos, embora alguns aspectos configuracionais da escrita não se adequem à norma padrão da língua portuguesa, se constroem para formar sentidos e para expressar compreensão de mundo própria; essa perspectiva, de alguma forma, pode subsidiar o ensino de português escrito como L2 a pessoas surdas.

This research addresses the need for a mobile application to teach Arabic sign language to deaf children in the Arab world. Therefore, the team develops an educational android application to teach deaf children the Arabic sign language using gamification. Also, the research aims to investigate the impact of using a mobile application for Arabic sign language on children’s learning performance. To achieve its goals, the research entails two main stages. The first stage focuses on building the mobile application based on the literature review and searching the Google and Apple stores for similar applications. In the second stage, an experiment is conducted on 10 deaf children—the experimental group adopted the mobile application for the learning process, whereas the control group followed the traditional learning approach. A pre-test and post-test were conducted, and the results show that children who used the game-based mobile application performed better in their quiz.

Communication between deaf and hearing individuals remains a persistent challenge despite advances in mobile and machine-learning technologies. Existing sign language interpretation tools often suffer from limited vocabulary, dependence on specialized hardware, lack of integrated text-to-speech (TTS) functionality, environmental sensitivity, and the absence of offline capability.

This study aimed to design, develop, and evaluate a mobile-based Android application capable of (a) translating American Sign Language (ASL) gestures into text and synthesized speech in real time, (b) providing a built-in tutorial of the alphabet and commonly used words, and (c) operating offline on standard Android smartphones.

A descriptive-developmental research design was employed, complemented by an Agile software development life cycle (Define–Design–Develop–Demonstrate–Release). The application was implemented in Java using Android Studio, with hand-landmark detection performed by Google's MediaPipe framework. Quality assurance was conducted through automated tools (Sauce Labs, BrowserStack, WeTest, SonarQube) covering functionality, usability, performance, security, compatibility, reliability, and maintainability. Manual testing examined the influence of lighting, background, hand position, and hand size. User Acceptance Testing (UAT) was administered to 30 purposively sampled respondents (15 deaf, 15 hearing). Data were analyzed using percentages, weighted mean, and standard deviation.

Functionality testing yielded a 90.0% pass rate. Security testing revealed no high- or medium-risk vulnerabilities. Compatibility testing confirmed seamless operation on Android 12, 13, and 14 (rated Excellent) and acceptable performance on Android 11 (rated Good). Recognition accuracy was approximately 75% under controlled conditions, with optimal performance observed in natural light, against solid backgrounds, and with the hand placed frontally to the camera. UAT results across the eight ISO/IEC 25010 quality characteristics (Functional Suitability, Performance Efficiency, Compatibility, Usability, Reliability, Security, Maintainability, and Portability) ranged from 4.55 to 4.72 on a 4-point scale, all corresponding to a "Strongly Agree" verbal interpretation.

The developed mobile application addresses key gaps identified in prior literature by integrating real-time gesture-to-text translation, on-device text-to-speech synthesis, an embedded tutorial, and offline functionality on standard Android hardware. While the system performs reliably under controlled conditions, future work should focus on expanding the lexicon, accommodating dynamic gestures, improving robustness to lighting and background variability, and supporting cross-platform deployment.

Sign language (SL) is an essential communication tool for learners with hearing impairments in promoting inclusive education. However, many institutions do not fully utilize SL that hinder deaf individuals’ access to educational services. This study employed descriptive-correlational research design to describe the academic performance of pre-service special education teachers and their level of sign language readiness, as well as to examine the relationship between these variables. Thirty-six out of 45 fourth year pre-service special education teachers willingly and completely responded the Google form with a response rate of 80%. Findings revealed that respondents demonstrated very good academic performance (M=90.28%, SD=2.54) in their sign language-related course and perceived a high level of sign language readiness (M=4.20, SD=0.82) across motivational, social, personal, and kinetic domains. However, results also indicated that there is no significant relationship between pre-service teachers’ academic performance and sign language readiness with p-value of .519, implying that their readiness is not directly influenced by their academic performance. It is recommended that teacher education institution may provide hands-on practice and enrichment program that further strengthen both the academic learning and practical application of sign language to ensure that future special education teachers are fully equipped to meet the needs of diverse learners.

The inclusion of Deaf students in regular classrooms has highlighted the need for the Chilean Sign Language Interpreter to act as a mediator between two languages: The sign language and oral language. Therefore, the objective of this study is to analyze the training and functions of the Chilean Sign Language Interpreter in the educational context. The instrument to achieve the objective was a semistructured interview with 12 Sign Language interpreters and 3 key informants in this field. The results show the informality in the training process, a series of prescribed functions, which distance themselves significantly from the non-prescribed ones, and a series of barriers that the educational context establishes for the interpretation. From these, the figure of the Chilean sign language facilitator emerges to help respond to the needs for access to learning by Deaf students in educational contexts.

Intraoperative direct cortical stimulation (DCS) is the gold standard technique to maximize the extent of resection of tumors located in eloquent areas. To date, there are three cases reported of awake mapping for language centers in deaf patients who could communicate only with sign language. We present the case of DCS in a deaf patient who could communicate vocally, native to American Sign Language and English, that underwent intraoperative awake mapping. DCS showed similar disruption of expressive phonology to both pictorial and gestural stimuli, confirming that sign language follows the same pattern as oral language.

The aim of this study was to determine the reliability and validity of the Latvian Sign Language Comprehension Test (LSLCT). Thirty-four deaf and hearing impaired (DHI) children (boys: 50%: girls: 50%) aged 8 to 12 years participated in the study. The children were attending specialist schools for DHI children and did not have any officially diagnosed developmental disorders. Item difficulty indices and item discrimination indices were established and the internal consistency and inter-rater reliability of the scale were assessed. The one-factor structure of the scale was verified. Teachers' ratings of children's sign language perception skills were used to determine the concurrent validity of the LSLCT. Construct validity was assessed on the basis of the correlations between LSLCT score and children's age, grade, gender, parental hearing status, age at exposure to sign language and signing age. LSLCT was shown to have high reliability and construct validity.

قامت هذه الدراسة باستطلاع آراء طلاب الدراسات العليا في تخصص أمراض النطق واللغة بهدف تحديد مدى وعيهم باضطرابات اللغةلغة الإشارة، مما يفتح المجال لنقاش محتمل حول الحاجة إلى تخصص أمراض اللغة الموقَّعة، بما في ذلك إمكانية تطوير تشخيصات وعلاجات مستقبلية قائمة على لغة الإشارة

قامت هذه الدراسة باستطلاع آراء طلاب الدراسات العليا في تخصص أمراض النطق واللغة بهدف تحديد مدى وعيهم باضطرابات لغة الإشارة، مما يفتح المجال لنقاش محتمل حول الحاجة إلى تخصص أمراض اللغة الموقَّعة، بما في ذلك إمكانية تطوير تشخيصات وعلاجات مستقبلية قائمة على لغة الإشارة.

One of the best ways of communication between deaf people and hearing people is based on sign language or so-called hand gestures. In the Arab society, only deaf people and specialists could deal with Arabic sign language, which makes the deaf community narrow and thus communicating with normal people difficult. In addition to that, studying the problem of Arabic sign language recognition (ArSLR) has been paid attention recently, which emphasizes the necessity of investigating other approaches for such a problem. This paper proposes a novel ArSLR scheme based on an unsupervised deep learning algorithm, a deep belief network (DBN) coupled with a direct use of tiny images, which has been used to recognize and classify Arabic alphabetical letters. The use of deep learning contributed to extracting the most important features that are sparsely represented and played an important role in simplifying the overall recognition task. In total, around 6,000 samples of the 28 Arabic alphabetic signs have been used after resizing and normalization for feature extraction. The classification process was investigated using a softmax regression and achieved an overall accuracy of 83.32%, showing high reliability of the DBN-based Arabic alphabetical character recognition model. This model also achieved a sensitivity and a specificity of 70.5% and 96.2%, respectively.

In recent Arabic standard language and Arabic dialectal texts, diacritics and short vowels are absent. There are some exceptions have been made for the Arabic beginner learner scripts, religious texts and as well as a significant political text. In addition, the text without diacritics is considered ambiguous due to numerous words with different diacritic marks seem identical. However, this paper we present a framework for segmenting diacritics from Arabic handwritten document by using region-based segmentation technique. Since Arabic handwritten and Mushaf Al-Quran contain many diacritical marks. Hence, the diacritics must be properly extracted from Arabic handwritten document to avoid losing some good features. Furthermore, the proposed framework is devised specifically to segment diacritics from Arabic handwritten image, thus there will be no feature extraction, feature selection, and classification processes included. Besides, we will present the methodology that is used to fulfil the objectives of this paper. The preprocessing phases will be explained and more specifically segmentation phase for segmenting diacritics which is the phase we concentrate more in this article. Lastly, we will identify the proposed technique region-based segmentation to facilitate our development throughout the experimental process.

We describe the literacy issues facing young deaf and hard of hearing (DHH) learners in Taiwan who are learning both a logographic and an alphabetic script. We describe the contextual factors that surround their reading and writing processes such as island demographics, reading achievement levels, and background language learning variables including deaf culture. We then describe and provide graphic illustrations and examples of the linguistic features of the written languages that DHH children are learning (Chinese and English) as well as the sign codes (Signed Chinese, Zhuyin finger alphabet, character signs, palm writing, and air writing) and the Zhuyin Fuhao written visual symbols. We suggest that contextual, cultural and linguistic factors need to be considered in the understanding of how DHH children learn to read and write as well as how they use their sign language and sign codes to build Chinese and English literacy skills. keywords deaf, hard of hearing, deaf education, literacy, Taiwanese Sign Language, Chinese, Deaf Culture All three authors prepare teachers and educational leaders of deaf and hard of hearing students.

Information about implementing a prereading program using the reciprocal training procedure in a school setting and on implications for educators, teacher educators and parents is presented. This instructional procedure grounded in Vygotsky's theory of language and learning is an interactive dialogue through which the teacher explicitly models four prereading skills: finger spelling, book reading, story reciting and word recognition during story-time session with experimental story books. Twenty-three prelingually deaf kindergarteners and first graders ranging in ages from five to eight with severe-toprofound and profound hearing losses participated in this study.

Alice, a deaf girl who was implanted after age three years of age was exposed to four weeks of storybook sessions conducted in American Sign Language (ASL) and speech (English). Two research questions were address: (1) how did she use her sign bimodal/bilingualism, codeswitching, and code mixing during reading activities and (2) what sign bilingual code-switching and code-mixing strategies did she use while attending to stories delivered under two treatments: ASL only and speech only. Retelling scores were collected to determine the type and frequency of her codeswitching/codemixing strategies between both languages after Alice was read to a story in ASL and in spoken English. Qualitative descriptive methods were utilized. Teacher, clinician and student transcripts of the reading and retelling sessions were recorded. Results showed Alice frequently used codeswitching and codeswitching strategies while retelling the stories retold under both treatments. Alice increased in her speech production retellings of the stories under both the ASL storyreading and spoken English-only reading of the story. The ASL storyreading did not decrease Alice's retelling scores in spoken English. Professionals are encouraged to consider the benefits of early sign bimodal/bilingualism to enhance the overall speech, language and reading proficiency of deaf children with cochlear implants.

Mi Diccionario visual con LSM y español esta dirigido a
personas sordas y oyentes. Es una herramienta que contribuye
de manera importante a la inclusión académica y
desarrollo de las competencias lingüísticas de las personas
con discapacidad auditiva pues favorece la comprensión de
las palabras (que encuentran un referente contextual y
señado).
Este diccionario relaciona las palabras y las imágenes
contextualizadas y su seña por eso es una herramienta útil
para los niños y jóvenes sordos, profesores, terapeutas de
lenguaje, padres de familia y funcionarios involucrados en la
educación especializada,. facilitando la fijación de los
conceptos en la mente.
Al final incluye algunos apartados que complementarán el
aprendizaje ya que apoya los temas escolares. Por ejemplo:
los continentes, los estados y sus capitales, las alcaldías de
la CDMX, personajes destacados de la historia, algunas
materias escolares, palabras de cortesía, uso del dinero,
vocabulario escolar, etc.

Ingles: A theoretical approach to manual verbs in Brazilian Sign Language (BSL) In this paper, we analyse whether manual verbs formed from nouns of instruments in Brazilian Sign Language (BSL) are examples of morphological derivation or noun incorporation (or both). The work is based on Quadros & Karnopp’s (2004) assumption that there are differences on the movement parameter in derivational processes forming verbs from nouns (eg. CHAIR versus SIT) and in noun incorporation forming verbs from nouns of instruments ( eg. SCISSORS versus CUT-WITH-SCISSORS). We conclude that manual verbs formed from nouns of instruments in BSL are formed by derivation, since they do not have specific signs for the verb and the sign of the instrument performs a verbal role in the syntactic structure (although whad not identified such difference on movement parameter in collected data). The cases of noun incorporation are restricted to object position (eg. EAT APPLE). Keywords: Brazilian Sign Language; Ge...

While most adversarial attacks are designed in image recognition, adversarial robustness in 3D action recognition is important to the real-world applications of healthcare, robotics, and surveillance. This paper reports the adversarial robustness achieved by a hybrid Spatiotemporal Graph Convolution Network, which can capture complex human motion dynamics with enhanced adversarial robustness. The model in this work is performed for the development of superior feature engineering techniques that take into consideration the pairwise joint distances and augmented angular velocities to further improve the clarity of motion patterns. This was followed by comprehensive adversarial training that provided robustness on noisy and perturbed inputs, hence making it perform excellently under challenging environments. The performance achieved in the proposed approach was 70% accuracy on the MHAD dataset, which proved to be much more robust and adaptable when compared to other state-of-the-art approaches. Such contributions to the field of action recognition offer an avenue toward much more reliable and accurate solutions for critical applications.

The lack of a standardized sign language, and the inability to communicate with the hearing community through sign language, are the two major issues confronting Pakistan's deaf and dumb society. In this research, we have proposed an approach to help eradicate one of the issues. Now, using the proposed framework, the deaf community can communicate with normal people. The purpose of this work is to reduce the struggles of hearing-impaired people in Pakistan. A Kinect-based Pakistan sign language (PSL) to Urdu language translator is being developed to accomplish this. The system’s dynamic sign language segment works in three phases: acquiring key points from the dataset, training a long short-term memory (LSTM) model, and making real-time predictions using sequences through openCV integrated with the Kinect device. The system’s static sign language segment works in three phases: acquiring an image-based dataset, training a model garden, and making real-time predictions using openC...

Our individual lives depend significantly on communication. Deaf and dumb people find it difficult to engage in normal conversations with other people. According to the World Health Organization, across the world 466 million people are affected (more than 5 percent of the world's population) and in that 34 million are kids. Consequently, they are unable to communicate efficiently with others. Sign language is the medium through which deaf people communicate, which involves the usage of gestures to express their feelings and emotions. A sign language gesture is a particular action of the hands, composed of a specific form. The main motive behind this device is to make disabled people feel confident in their communication and also to get rid of their fear or any kind of awkwardness while communicating with others. Five LDR sensors are used in such a way that each LDR sensor is fixed at the tip of glove finger. The gesture of the hand plays a vital role. The micro-controller decodes the gestures. The words which are fed are measured using each particular gesture (i.e., making entirely different finger positions) of LDR sensors. The system analyzes those gestures and synthesizes the display the message for common people to recognize. In this system, hand gloves are used through which the hand gestures are measured directly and accurately.

Sign language is a visual language. Research into Sign language, including in related areas such as linguistics and engineering is lagging behind spoken language. One reason for this is the absence of a common database available to researchers from different areas. This paper defines how to words are selected for inclusion in the sign language database, how to select informants, what format the data are included in and how to record the data. Also, the database includes threedimensional behavioral data based on dialogue. The following explains the results.

This paper tackles a problem of analyzing the well-formedness of syllables in Japanese Sign Language (JSL). We formulate the problem as a classification problem that classifies syllables into wellformed or ill-formed. We build a data set that contains hand-coded syllables and their well-formedness. We define a finegrained feature set based on the handcoded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained classifier. We also perform pseudo active learning to investigate the applicability of active learning in analyzing syllables. In the experiments, the best classifier with our combinatorial features achieved the accuracy of 87.0%. The pseudo active learning is also shown to be effective showing that it could reduce about 84% of training instances to achieve the accuracy of 82.0% when compared to the model without active learning.

A inclusão do aluno surdo é um tema ainda pouco discutido no contexto educacional, pois exige mudançade atitude, é um processo de transformação. Devido ao fato do aluno surdo não corresponder aos padrões de homogeneização, o seu processo ensino-aprendizagem é estigmatizado e inferiorizado. Tendo este artigo como tema o Bilinguismo e Inclusão escolar dos alunos surdos, elaborou-se como problemática de pesquisa: Como o bilinguismo pode contribuir na educação dos alunos surdos? O objetivo geral é compreender de que modo o bilinguismo pode contribuir na educação de surdos. Como objetivos específicos foram delimitados: analisar asimplicações da surdez no processo ensino-aprendizado; descrever a proposta de educação bilíngue no Brasil no contexto da surdez e comparar a proposta do bilinguismo com a inclusão escolar dos alunos surdos no contexto educacional vigente. A pesquisa do trabalho é de natureza básica, qualitativa, em relação aos objetivos é descritiva e os procedimentos técnicos b...

This study is the first to investigate the prevalence and variety of legal contexts where ASL-English interpreters work in the United States. Effective interpreting service ensures that deaf individuals' civic and constitutional rights are protected when they engage in the U.S. legal system. We sought to understand the current landscape of legal interpreting practice by ASL-English interpreters to better inform professional training and allocation of resources. There is a severe, national shortage of legally qualified sign language interpreters and a need for regular educational pathways and professional development to train generalist interpreters in specialized legal settings. An online survey was distributed to sign language interpreters who work in the United States and its territories. Family, juvenile, and traffic-related settings were common among the type of court and cases where interpreters reportedly work. In the courtroom, the introductory procedures were the most commonly encountered. Outside of the courtroom, the most frequent procedures were court-oriented programs, attorney-client conferences, and plea bargaining. The qualification profile of practitioners indicates that legal specialist training was significantly more prevalent in lieu of or in addition to the suspended national Registry of Interpreters for the Deaf specialist legal certification. Findings indicate that a significant proportion of legal interpreting work is done outside of the trial setting, thus educational opportunities and training resources need to address the range of legal settings to best prepare interpreters for professional assignments.

In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human-computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM's uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

Sign languages display remarkable crosslinguistic consistencies in the use of handshapes. In particular, handshapes used in classifier predicates display a consistent pattern in finger complexity: classifier handshapes representing objects display more finger complexity than those representing how objects are handled. Here we explore the conditions under which this morphophonological phenomenon arises. In Study 1, we ask whether hearing individuals in Italy and the United States, asked to communicate using only their hands, show the same pattern of finger complexity found in the classifier handshapes of two sign languages: Italian Sign Language (LIS) and American Sign Language (ASL). We find that they do not: gesturers display more finger complexity in handling handshapes than in object handshapes. The morphophonological pattern found in conventional sign languages is therefore not a codified version of the pattern invented by hearing individuals on the spot. In Study 2, we ask whether continued use of gesture as a primary communication system results in a pattern that is more similar to the morphophonological pattern found in conventional sign languages or to the pattern found in gesturers. Homesigners have not acquired a signed or spoken language and instead use a selfgenerated gesture system to communicate with their hearing family members and friends. We find that homesigners pattern more like signers than like gesturers: their finger complexity in object handshapes is higher than that of gesturers (indeed as high as signers); and their finger complexity in handling handshapes is lower than that of gesturers (but not quite as low as signers). Generally, our findings indicate two markers of the phonologization of handshape in sign languages: increasing finger complexity in object handshapes, and decreasing finger complexity in handling handshapes. These first indicators of phonology appear to be present in individuals developing a gesture system without benefit of a linguistic community. Finally, we propose that iconicity, morphology and phonology each play an important role in the system of sign language classifiers to create the earliest markers of phonology at the morphophonological interface.

AI-driven educational technologies are expanding rapidly, yet their design rarely reflects the linguistic and infrastructural realities of Deaf learners in low-resource contexts. This qualitative study investigates how teachers, parents, and Deaf students in Bangladesh navigate fragile visual access, inconsistent Bangla Sign Language, and unreliable technology in everyday learning. Through interviews and focus groups with 13 teachers, eight parents, and five Deaf students, we show how small disruptions in sightlines, pacing, or sign clarity can quickly collapse comprehension, making accessibility a condition that must be constantly protected. We identify opportunities for AI to act as access support by stabilizing visual information, ensuring teacher-validated Bangla Sign Language, enabling offline use, and protecting emotional safety when seeking help. We contribute a model of learning continuity that explains how visual, linguistic, and affective stability interact in Deaf education and offer concrete design directions for AI-driven learning tools in the Global South. CCS Concepts • Human-centered computing → Empirical studies in HCI; User studies.

As video has become the dominant mode of content on platforms such as YouTube, TikTok, and Instagram, captioning has emerged as a critical factor for accessibility, engagement, and visibility. While prior studies have examined different types of social media video captions or communities' captioning usage, a systematic synthesis has not been undertaken, leading to the risk of proposing interventions that overlook core platform constraints or miss critical accessibility needs. This paper reviews 36 peer-reviewed papers published between 2015 and 2025 across fields such as Human-Computer Interaction (HCI), accessibility, media studies, education, and language learning. We note that captions operate as collective infrastructure co-produced by viewers, creators, and platforms. Deaf and Hard of Hearing (DHH), neurodivergent, and multilingual viewers depend on captions and increasingly expect mechanisms for feedback, while creators face inadequate tool support. Building on these insights, we propose the framework of Participatory Captioning and suggest design implications, highlighting future directions for social media video caption research. CCS Concepts • Human-centered computing → Accessibility systems and tools; HCI design and evaluation methods; Empirical studies in HCI ; Social media.

As video has become the dominant mode of content on platforms such as YouTube, TikTok, and Instagram, captioning has emerged as a critical factor for accessibility, engagement, and visibility. While prior studies have examined different types of social media video captions or communities' captioning usage, a systematic synthesis has not been undertaken, leading to the risk of proposing interventions that overlook core platform constraints or miss critical accessibility needs. This paper reviews 36 peer-reviewed papers published between 2015 and 2025 across fields such as Human-Computer Interaction (HCI), accessibility, media studies, education, and language learning. We note that captions operate as collective infrastructure co-produced by viewers, creators, and platforms. Deaf and Hard of Hearing (DHH), neurodivergent, and multilingual viewers depend on captions and increasingly expect mechanisms for feedback, while creators face inadequate tool support. Building on these insights, we propose the framework of Participatory Captioning and suggest design implications, highlighting future directions for social media video caption research. CCS Concepts • Human-centered computing → Accessibility systems and tools; HCI design and evaluation methods; Empirical studies in HCI ; Social media.

In 2011, Carolyn McCaskill et al. published a study of Black American Sign Language (Black ASL or BASL). The authors highlighted the fact that, just as Hearing Black Americans have an idiom and culture that is clearly distinctive from the "White mainstream," Black Deaf Americans have such a separate idiom and culture as well, only that theirs is purely visual. We still know little about this visual Black Deaf culture and its history. In this article, I provide a much-needed overview of Black Deaf history from the U.S. Civil War to the present day that connects the available, somewhat scattered pieces of information. Proceeding chronologically, I focus on major stages of development, including, first of all, the role of segregated schools for the deaf in the U.S. South between the 1860s and 1970s. Moreover, I examine the unique contributions of Black Deaf Americans to the Civil Rights Movement since the 1950s and point to the rising self-awareness of Black Deaf people in the course of the 1960s and 1970s, which culminated in 1980 in the establishment of a national interest group, the National Black and Deaf Advocates (NBDA). In moving along, I contextualize Black Deaf role models like Black Deaf missionary Andrew Foster. I close by exploring how the Internet has opened up new opportunities for spreading Black Deaf group awareness as a basis for studying Black Deaf culture and history further.

Effective communication can be challenging when individuals are unfamiliar with each other's spoken language. This problem becomes more complex when the languages involved differ in modality such as spoken language and sign language. Machine translation has emerged as a valuable tool for bridging this communication gap in various scenarios. This research addresses the unique challenges presented by cross-modal machine translation systems, specifically in translating from spoken language to sign language. While spoken languages typically exhibit linear linguistic characteristics, sign languages employ simultaneous expressions through manual, non-manual, and spatial features. This divergence in delivery mechanisms necessitates careful consideration in the development of a translation system. The primary objective of this research is to develop a framework that translates simple Marathi sentences into Indian sign language, incorporating computational techniques to account for the simultaneous nature of sign language grammar. A comprehensive linguistic analysis is conducted to identify the specific divergences between Marathi and Indian sign language. The system is evaluated using a database comprising vocabulary relevant to daily communication. This paper provides a detailed account of the system architecture, experimental setup, results, and evaluation. The development of a cross-modal translation system holds significant potential in bridging the communication gap between individuals using spoken languages and those who rely on sign languages as their primary means of communication. The system-generated ISL can facilitate communication for hearing-impaired persons and, thus, it contributes to the inclusivity of hearing-impaired communities in the mainstream.

Communication barriers significantly impede social participation for the estimated 466 million individuals worldwide with disabling hearing loss and the additional millions affected by speech and motor impairments. Existing assistive technologies are characterised by three persistent deficiencies: singlemodality coverage that obliges users to manage multiple fragmented tools, dependence on cloud-based inference that compromises user privacy, and prohibitive acquisition costs that restrict access in lowresource settings. This paper presents the Multi-Modal Assistive Communication System (MMACS), a unified browser-based artificial intelligence platform that simultaneously addresses all three deficiencies. MMACS integrates American Sign Language (ASL) gesture recognition (30 signs, ~10-15% of the full lexicon), AI-driven lip reading through viseme detection, and interactive Morse code translation within a single web application requiring no software installation. The system employs MediaPipe for realtime hand and facial landmark detection, TensorFlow.js for client-side neural network inference, and the Web Speech API for voice synthesisall executing locally on the user's device to ensure data privacy. Empirical evaluation with 50 participants (18 with hearing impairments, 12 with speech impairments, 8 with motor impairments, 12 neurotypical controls) demonstrated ASL recognition accuracy of 94.2% (±2.1%), lip reading accuracy of 87.8% (±3.4%), and sub-200 ms end-to-end latency across all modalities. The mobilefirst responsive architecture ensures compatibility across all major browser engines on desktop, tablet, and mobile form factors. A post-study satisfaction survey recorded a 92% positive rating, indicating strong perceived utility in controlled settings. MMACS establishes a reproducible, open-source benchmark for privacy-preserving assistive communication technology and furnishes a principled framework for future multimodal human-computer interaction research.