Search results for: sign structure

Authors: Sriparna Bandopadhyay

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It is known that irreducible sign patterns in general may not allow diagonalizability and in particular irreducible sign patterns with minimum rank greater than or equal to 4. It is also known that every irreducible sign pattern matrix with minimum rank of 2 allow diagonalizability with rank of 2 and the maximum rank of the sign pattern. In general sign patterns with minimum rank of 3 may not allow diagonalizability if the condition of irreducibility is dropped, but the problem of whether every irreducible sign pattern with minimum rank of 3 allows diagonalizability remains open. In this paper it is shown that irreducible sign patterns with minimum rank of 3 under certain conditions on the underlying graph allow diagonalizability. An alternate proof of the results that every sign pattern matrix with minimum rank of 2 and no zero lines allow diagonalizability with rank of 2 and also that every full sign pattern allows diagonalizability with all permissible ranks of the sign pattern is given. Some open problems regarding composite cycles in an irreducible symmetric sign pattern that support of a rank principal certificate are also answered.

Keywords: irreducible sign patterns, minimum rank, symmetric sign patterns, rank -principal certificate, allowing diagonalizability

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Authors: WooYoung Jung, Buntheng Chhorn, Min-Gi Kim

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Recently, damage to domestic facilities by strong winds and typhoons are growing. Therefore, this study focused on sign structure among various vulnerable facilities. The evaluation of the wind fragility was carried out considering the destruction of the anchor, which is one of the various failure modes of the sign structure. The performance evaluation of the anchor was carried out to derive the wind fragility. Two parameters were set and four anchor types were selected to perform the pull-out and shear tests. The resistance capacity was estimated based on the experimental results. Wind loads were estimated using Monte Carlo simulation method. Based on these results, we derived the wind fragility according to anchor type and wind exposure category. Finally, the evaluation of the wind fragility was performed according to the experimental parameters such as anchor length and anchor diameter. This study shows that the depth of anchor was more significant for the safety of structure compare to diameter of anchor.

Keywords: sign structure, wind fragility, set anchor, pull-out test, shear test, Monte Carlo simulation

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Authors: Wanessa G. Oliveira, Fernando C. Capovilla

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Capovilla and Raphael’s Libras Dictionary documents a corpus of 4,200 Brazilian Sign Language (Libras) signs. Duduchi and Capovilla’s software SignTracking permits users to retrieve signs even when ignoring the gloss corresponding to it and to discover the meaning of all 4,200 signs sign simply by clicking on graphic menus of the sign characteristics (phonemes). Duduchi and Capovilla have discovered that the ease with which any given sign can be retrieved is an inverse function of the average popularity of its component phonemes. Thus, signs composed of rare (distinct) phonemes are easier to retrieve than are those composed of common phonemes. SignTracking offers a means of computing the average popularity of the phonemes that make up each one of 4,200 signs. It provides a precise measure of the degree of ease with which signs can be retrieved, and sign meanings can be discovered. Duduchi and Capovilla’s logarithmic model proved valid: The degree with which any given sign can be retrieved is an inverse function of the arithmetic mean of the logarithm of the popularity of each component phoneme. Capovilla, Raphael and Mauricio’s New Libras Dictionary documents a corpus of 10,400 Libras signs. The present analysis revealed Libras DNA structure by mapping the incidence of 501 sign phonemes resulting from the layered distribution of five parameters: 163 handshape phonemes (CherEmes-ManusIculi); 34 finger shape phonemes (DactilEmes-DigitumIculi); 55 hand placement phonemes (ArtrotoToposEmes-ArticulatiLocusIculi); 173 movement dimension phonemes (CinesEmes-MotusIculi) pertaining to direction, frequency, and type; and 76 Facial Expression phonemes (MascarEmes-PersonalIculi).

Keywords: Brazilian sign language, lexical retrieval, libras sign, sign phonology

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Authors: Neha Kulshreshtha

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This paper examines the patterns of 'Mouthing', a non-manual marker, and its distribution in Indian Sign Language (ISL). Linguistic research in Indian Sign Language is an emerging field where much is needed to be done. The little research which has happened focuses on the structure of ISL in terms of physical or manual markers, therefore a study of mouthing patterns would give an insight into the distribution of this particular non-manual marker. Data has been collected with the help of native ISL users through various techniques in which natural signs can be captured, for example, storytelling, informal conversations etc. The aim of the study is to find out the various situations where mouthing is used. Sometimes, the mouthing is not actually the articulation of the word as spoken in the local languages. The paper aims to find out whether the mouthing patterns in ISL are influenced by any local language or they are independent of any influence from the local language or both. Mouthing patterns have been studied in many sign languages and an investigation into ISL will reveal whether it falls in pattern with the other sign languages.

Keywords: Indian sign language, mouthing, non-manual marker, spoken language influence

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Authors: Marcio Leal, Marta Villamil

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Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%.

Keywords: artificial neural network, computer vision, dynamic time warping, infrared, sign language recognition

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Authors: Joon-Yeop Na, Sang-Keun Baik, Kyu-Soo Chong

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Road sign sizes are related to their support and foundation, and the large-scale support that is generally installed at roadsides can cause inconvenience to pedestrians and damage the urban landscape. The most influential factor in determining the support and foundation of road signs is the wind load. In this study, we introduce a hole-type road sign to analyze its effects on reducing wind load. A hole-type road sign reduces the drag coefficient that is applied when considering the air and fluid resistance of a plate when the wind pressure is calculated, thus serving as an effective option for lightening the weights of road sign structures. A hole-type road sign is punctured with a perforator. Furthermore, the size of the holes and their distance is determined considering the damage to characters, the poor performance of reflective sheets, and legibility. For the calculation of the optimal specification of a hole-type road sign, we undertook a theoretical examination for reducing the wind loads on hole-type road signs, and analyzed the bending and reflectivity of sample road sign plates. The analytic results confirmed that a hole-type road sign sample that contains holes of 6 mm in diameter with a distance of 18 mm between the holes shows reflectivity closest to that of existing road signs; moreover, the average bending moment resulted in a reduction of 4.24%, and the support’s diameter is reduced by 40.2%.

Keywords: hole type, road sign, weight lightening, wind load

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Authors: Maria Tagarelli De Monte

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Since its formal recognition in 2021, Italian Sign Language (LIS) and interpreters’ education has become a topic for higher education in Italian universities. In April 2022, Italian universities have been invited to present their proposals to create sign language courses for interpreters’ training for both LIS and tactile LIS. As a result, a few universities have presented a three-year course leading candidate students from the introductory level to interpreters. In such a context, there is an open debate not only on the fact that three years may not be enough to prepare skillful interpreters but also on the need to refer to international standards in the definition of the training path to follow. Among these, are the Common European Framework of Reference (CEFR) for languages and Dublin’s descriptors. This contribution will discuss the potentials and the challenges given by LIS training in academic settings, by comparing traditional studies to the requests coming from universities. Particular attention will be given to the use of CEFR as a reference document for the Italian Sign Language Curriculum. Its use has given me the chance to reflect on how LIS can be taught in higher education, and the adaptations that need to be addressed to respect the visual-gestural nature of sign language and the formal requirements of academic settings.

Keywords: Italian sign language, higher education, sign language curriculum, interpreters education, CEFR

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Authors: Maria da Gloria Guara-Tavares

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This article reports a synthesis of the research in Brazilian Sign Language conducted from 2000 to 2017. The objective of the synthesis was to identify the most researched areas and the most used methodologies. Articles published in three Brazilian journals of Translation Studies, unpublished dissertations and theses were included in the analysis. Abstracts and the method sections of the papers were scrutinized. Sixty studies were analyzed, and overall results indicate that the research in Brazilian Sign Language has been fragmented in several areas such as linguistic aspects, facial expressions, subtitling, identity issues, bilingualism, and interpretation strategies. Concerning research methods, the synthesis reveals that most research is qualitative in nature. Moreover, results show that the cognitive aspects of Brazilian Sign Language seem to be poorly explored. Implications for a future research agenda are also discussed.

Keywords: Brazilian sign language, qualitative methods, research agenda, synthesis

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

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Authors: Tesfaye Basha

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The purpose of this study was to examine the sign language and academic achievement of deaf students in special and inclusive primary schools of Southern Ethiopia. The study used a mixed-method to collect varied data. The study contained Signed Amharic and English skill tasks, questionnaire, 8th-grade Primary School Leaving Certificate Examination results, classroom observation, and interviews. For quantitative (n=70) deaf students and for qualitative data collection, 16 participants were involved. The finding revealed that the limitation of sign language is a problem in signing and academic achievements. This displays that schools are not linguistically rich to enable sign language achievement for deaf students. Moreover, the finding revealed that the contribution of Total Communication in the growth of natural sign language for deaf students was unsatisfactory. The results also indicated that special schools of deaf students performed better sign language skills and academic achievement than inclusive schools. In addition, the findings revealed that high signed skill group showed higher academic achievement than the low skill group. This displayed that sign language skill is highly associated with academic achievement. In addition, to qualify deaf students in sign language and academics, teacher institutions must produce competent teachers on how to teach deaf students with sign language and literacy skills.

Keywords: academic achievement, inclusive school, sign language, signed Amharic, signed English, special school, total communication

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Authors: Gal Belsitzman, Rose Stamp, Atay Citron, Wendy Sandler

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Sign languages are considered endangered. The vitality of sign languages is compromised by its unique sociolinguistic situation, in which hearing parents that give birth to deaf children usually decide to cochlear implant their child. Therefore, these children don’t acquire their natural language – Sign Language. Despite this, many sign languages, such as Israeli Sign Language (ISL) are thriving. The continued survival of similar languages under threat has been associated with the remarkable resilience of the language community. In particular, deaf literary traditions are central in reminding the community of the importance of the language. One example of a deaf literary tradition which has received increased popularity in recent years is deaf theatre. The Ebisu Sign Language Theatre Laboratory, developed as part of the multidisciplinary Grammar of the Body Research Project, is the first deaf theatre company in Israel. Ebisu Theatre combines physical theatre and sign language research, to allow for a natural laboratory to analyze the creative use of the body. In this presentation, we focus on the recent theatre production called ‘Their language’ which tells of the struggle faced by the deaf community to use their own natural language in the education system. A thorough analysis unravels how linguistic properties are integrated with the use of poetic devices and physical theatre techniques in this performance, enabling wider access by both deaf and hearing audiences, without interpretation. Interviews with the audience illustrate the significance of this art form which serves a dual purpose, both as empowering for the deaf community and educational for the hearing and deaf audiences, by raising awareness of community-related issues.

Keywords: deaf theatre, empowerment, language revitalization, sign language

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Authors: Vidhi Jain

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Communication between speakers and non-speakers of American Sign Language (ASL) can be problematic, inconvenient, and expensive. This project attempts to bridge the communication gap by designing a portable glove that captures the user’s ASL gestures and outputs the translated text on a smartphone. The glove is equipped with flex sensors, contact sensors, and a gyroscope to measure the flexion of the fingers, the contact between fingers, and the rotation of the hand. The glove’s Arduino UNO microcontroller analyzes the sensor readings to identify the gesture from a library of learned gestures. The Bluetooth module transmits the gesture to a smartphone. Using this device, one day speakers of ASL may be able to communicate with others in an affordable and convenient way.

Keywords: sign language, morse code, convolutional neural network, American sign language, gesture recognition

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Authors: L. Cruz-Lopes, M. Sell, P. Escudeiro, B. Esteves

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It might be difficult for deaf people to communicate since spoken and written languages are different from sign language. When it comes to getting information, going to places of cultural heritage, or using services and infrastructure, there is a clear lack of inclusiveness. By creating assistive technology that enables deaf individuals to get around communication hurdles and encourage inclusive tourism, the ICHT- Inclusive Cultural Heritage Tourism initiative hopes to increase knowledge of sign language. The purpose of the Inclusive Cultural Heritage Tourism (ICHT) project is to develop online and on-site sign language tools and material for usage at popular tourist destinations in the northern region of Portugal, including Torre dos Clérigos, the Lello bookstore, Maia Zoo, Porto wine cellars, and São Pedro do Sul (Viseu) thermae. The ICHT system consists of an application using holography, a mobile game, an online platform for collaboration with deaf and hearing users, and a collection of International Sign training courses. The project also offers a prospect for a more inclusive society by introducing a method of teaching sign languages to tourism industry professionals. As a result, the teaching and learning of sign language along with the assistive technology tools created by the project sets up an inclusive environment for the deaf community, producing results in the area of automatic sign language translation and aiding in the global recognition of the Portuguese tourism industry.

Keywords: inclusive tourism, games, international sign training, deaf community

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Authors: Dení Stincer Gómez, Zuraya Monroy Nasr

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From the epistemology of emotions, one of the topics of current reflection is the function that emotions fulfill in the rational processes involved in scientific activity. So far, three functions have been assigned to them: selective, heuristic, and carriers of content. In this last function, it is argued that emotions, like our perceptual organs, contribute relevant content to reasoning, which is then converted into linguistic statements or graphic representations. In this paper, of a qualitative and philosophical nature, arguments are provided for two hypotheses 1) if emotions provide content to the mind, which then translates it into language or representations, then it is important to take up the idea of the Saussurean linguistic sign to understand this process. This sign has two elements: the signified and the signifier. Emotions would provide meanings, and reasoning creates the signifier, and 2) the meanings provided by emotions are properties and qualities of phenomena generally not accessible to the sense organs. These meanings must be imagined, and the imagination is nurtured by the feeling that "maybe this is the way." One way to access the content provided by emotions can be through imagined scientific drawings. The atomic models created since Thomson, the structure of crystals by René Just, the representations of lunar eclipses by Johannes, fractal geometry, and the structure of DNA, among others, have resulted fundamentally from the imagination. These representations, not provided by the sense organs, seem to come from the emotional involvement of scientists in their desire to understand, explain and discover.

Keywords: emotions, epistemic functions of emotions, scientific drawing, linguistic sign

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Authors: Hsiu Tan Liu, Chun Jung Liu

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It has multiple purposes to develop a sign language receptive skills test. For example, this test can be used to be an important tool for education and to understand the sign language ability of deaf children. There is no available test for these purposes in Taiwan. Through the discussion of experts and the references of standardized Taiwanese Sign Language Receptive Test for adults and adolescents, the frame of Taiwanese Sign Language Receptive Skills Test (TSL-RST) for deaf children was developed, and the items were further designed. After multiple times of pre-trials, discussions and corrections, TSL-RST is finally developed which can be conducted and scored online. There were 33 deaf children who agreed to be tested from all three deaf schools in Taiwan. Through item analysis, the items were picked out that have good discrimination index and fair difficulty index. Moreover, psychometric indexes of reliability and validity were established. Then, derived the regression formula was derived which can predict the sign language receptive skills of deaf children. The main results of this study are as follows. (1). TSL-RST includes three sub-test of vocabulary comprehension, syntax comprehension and paragraph comprehension. There are 21, 20, and 9 items in vocabulary comprehension, syntax comprehension, and paragraph comprehension, respectively. (2). TSL-RST can be conducted individually online. The sign language ability of deaf students can be calculated fast and objectively, so that they can get the feedback and results immediately. This can also contribute to both teaching and research. The most subjects can complete the test within 25 minutes. While the test procedure, they can answer the test questions without relying on their reading ability or memory capacity. (3). The sub-test of the vocabulary comprehension is the easiest one, syntax comprehension is harder than vocabulary comprehension and the paragraph comprehension is the hardest. Each of the three sub-test and the whole test are good in item discrimination index. (4). The psychometric indices are good, including the internal consistency reliability (Cronbach’s α coefficient), test-retest reliability, split-half reliability, and content validity. The sign language ability are significantly related to non-verbal IQ, the teachers’ rating to the students’ sign language ability and students’ self-rating to their own sign language ability. The results showed that the higher grade students have better performance than the lower grade students, and students with deaf parent perform better than those with hearing parent. These results made TLS-RST have great discriminant validity. (5). The predictors of sign language ability of primary deaf students are age and years of starting to learn sign language. The results of this study suggested that TSL-RST can effectively assess deaf student’s sign language ability. This study also proposed a model to develop a sign language tests.

Keywords: comprehension test, elementary school, sign language, Taiwan sign language

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Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay

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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

Keywords: annotated facial expression dataset, gesture recognition, sequenced facial expression dataset, sign language recognition

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Authors: Sugandhi, Parteek Kumar, Sanmeet Kaur

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Sign Language (SL) is used by deaf and other people who cannot speak but can hear or have a problem with spoken languages due to some disability. It is a visual gesture language that makes use of either one hand or both hands, arms, face, body to convey meanings and thoughts. SL automation system is an effective way which provides an interface to communicate with normal people using a computer. In this paper, an avatar based dictionary has been proposed for text to Indian Sign Language (ISL) generation system. This research work will also depict a literature review on SL corpus available for various SL s over the years. For ISL generation system, a written form of SL is required and there are certain techniques available for writing the SL. The system uses Hamburg sign language Notation System (HamNoSys) and Signing Gesture Mark-up Language (SiGML) for ISL generation. It is developed in PHP using Web Graphics Library (WebGL) technology for 3D avatar animation. A multilingual ISL dictionary is developed using HamNoSys for both English and Hindi Language. This dictionary will be used as a database to associate signs with words or phrases of a spoken language. It provides an interface for admin panel to manage the dictionary, i.e., modification, addition, or deletion of a word. Through this interface, HamNoSys can be developed and stored in a database and these notations can be converted into its corresponding SiGML file manually. The system takes natural language input sentence in English and Hindi language and generate 3D sign animation using an avatar. SL generation systems have potential applications in many domains such as healthcare sector, media, educational institutes, commercial sectors, transportation services etc. This research work will help the researchers to understand various techniques used for writing SL and generation of Sign Language systems.

Keywords: avatar, dictionary, HamNoSys, hearing impaired, Indian sign language (ISL), sign language

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Authors: Anželika Teresė

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In order to solve issues in sign language linguistics, address matters pertaining to maintaining high quality of sign language (SL) translation, contribute to dispelling misconceptions about SL and deaf people, and raise awareness and understanding of the deaf community heritage, this presentation discusses literature in Lithuanian Sign Language (LSL) and inherent metaphors that are created by using the phonological parameter –handshape, location, movement, palm orientation and nonmanual features. The study covered in this presentation is twofold, involving both the micro-level analysis of metaphors in terms of phonological parameters as a sub-lexical feature and the macro-level analysis of the poetic context. Cognitive theories underlie research of metaphors in sign language literature in a range of SL. The study follows this practice. The presentation covers the qualitative analysis of 34 pieces of LSL literature. The analysis employs ELAN software widely used in SL research. The target is to examine how specific types of each phonological parameter are used for the creation of metaphors in LSL literature and what metaphors are created. The results of the study show that LSL literature employs a range of metaphors created by using classifier signs and by modifying the established signs. The study also reveals that LSL literature tends to create reference metaphors indicating status and power. As the study shows, LSL poets metaphorically encode status by encoding another meaning in the same sign, which results in creating double metaphors. The metaphor of identity has been determined. Notably, the poetic context has revealed that the latter metaphor can also be identified as a metaphor for life. The study goes on to note that deaf poets create metaphors related to the importance of various phenomena significance of the lyrical subject. Notably, the study has allowed detecting locations, nonmanual features and etc., never mentioned in previous SL research as used for the creation of metaphors.

Keywords: Lithuanian sign language, sign language literature, sign language metaphor, metaphor at the phonological level, cognitive linguistics

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Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

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This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

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Authors: Mohammad Shams Esfand Abadi, Mehrdad Zalaghi, Reza ebrahimpour

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In this paper, we propose a sign adaptive filter algorithm with the ability of dynamic selection of subband filters which leads to low computational complexity compared with conventional sign subband adaptive filter (SSAF) algorithm. Dynamic selection criterion is based on largest reduction of the mean square deviation at each adaption. We demonstrate that this simple proposed algorithm has the same performance of the conventional SSAF and somewhat faster than it. In the presence of impulsive interferences robustness of the simple proposed algorithm as well as the conventional SSAF and outperform the conventional normalized subband adaptive filter (NSAF) algorithm. Therefore, it is preferred for environments under impulsive interferences. Simulation results are presented to verify these above considerations very well have been achieved.

Keywords: acoustic echo cancellation (AEC), normalized subband adaptive filter (NSAF), dynamic selection subband adaptive filter (DS-NSAF), sign subband adaptive filter (SSAF), impulsive noise, robust filtering

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave radar (FMCW) extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a new detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a BP neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise and accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal-to-noise ratio of the sign signals.

Keywords: frequency modulated continuous wave radar, ICEEMDAN, BP neural network, vital signs signal

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Authors: Nermeen El Kashef, Yasser Fouad, Khaled Mahar

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No AI-complete system can model the human brain or behavior, without looking at the totality of the whole situation and incorporating a combination of senses. This paper proposes a Pattern Recognition model based on Simulation of Chemical Senses (SCS) for separation and classification of sign language. The model based on human taste controlling strategy. The main idea of the introduced model is motivated by the facts that the tongue cluster input substance into its basic tastes first, and then the brain recognizes its flavor. To implement this strategy, two level architecture is proposed (this is inspired from taste system). The separation-level of the architecture focuses on hand posture cluster, while the classification-level of the architecture to recognizes the sign language. The efficiency of proposed model is demonstrated experimentally by recognizing American Sign Language (ASL) data set. The recognition accuracy obtained for numbers of ASL is 92.9 percent.

Keywords: artificial intelligence, biocybernetics, gustatory system, sign language recognition, taste sense

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Authors: Ma Lei-Lei, Zhou You

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Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

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Authors: Rania Ahmed Kadry Abdel Gawad Birry, Mohamed El-Habrouk

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This paper presents a brief survey of the techniques used for sign language recognition along with the types of sensors used to perform the task. It presents a modified method for identification of an isolated sign language gesture using Microsoft Kinect with the OpenNI framework. It presents the way of extracting robust features from the depth image provided by Microsoft Kinect and the OpenNI interface and to use them in creating a robust and accurate gesture recognition system, for the purpose of ASL translation. The Prime Sense’s Natural Interaction Technology for End-user - NITE™ - was also used in the C++ implementation of the system. The algorithm presents a simple finger counting algorithm for static signs as well as directional Finite State Machine (FSM) description of the hand motion in order to help in translating a sign language gesture. This includes both letters and numbers performed by a user, which in-turn may be used as an input for voice pronunciation systems.

Keywords: American sign language, finger counting, hand tracking, Microsoft Kinect

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Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

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Authors: Ankita Wadhawan, Parteek Kumar, Umesh Kumar

Abstract:

Gesture is any body movement that expresses some attitude or any sentiment. Gestures as a sign language are used by deaf people for conveying messages which helps in eliminating the communication barrier between deaf people and normal persons. Nowadays, everybody is using mobile phone and computer as a very important gadget in their life. But there are some physically challenged people who are blind/deaf and the use of mobile phone or computer like device is very difficult for them. So, there is an immense need of a system which works on body gesture or sign language as input. In this research, Microsoft Kinect Sensor, SDK V2 and Hidden Markov Toolkit (HTK) are used to recognize the object, motion of object and human body joints through Touch less NUI (Natural User Interface) in real-time. The depth data collected from Microsoft Kinect has been used to recognize gestures of Indian Sign Language (ISL). The recorded clips are analyzed using depth, IR and skeletal data at different angles and positions. The proposed system has an average accuracy of 85%. The developed Touch less NUI provides an interface to recognize gestures and controls the cursor and click operation in computer just by waving hand gesture. This research will help deaf people to make use of mobile phones, computers and socialize among other persons in the society.

Keywords: gesture recognition, Indian sign language, Microsoft Kinect, natural user interface, sign language

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Authors: Aniekan James Akpan

Abstract:

The use of signs and symbols to represent cardinal information is inalienable among African communities. It has been the tenable instrument of cultural transmission for decades, but in the current transposal of Western values, a good number of communicating codes and symbolic images have been gradually relegated to the background. This paper discusses the regeneration of cultural artifacts and sign languages in Nigerian films using Johnpaul Rebecca and Ayamma as models in a qualitative research based on Charles Sanders Pierce's Semiotics as well as quantitative methods of survey using questionnaire and focus group discussion as instruments to obtain data. With a population of 2,343 viewers of the movies used for the study and a sample size of 322 respondents using Philip Meyer’s guide, findings show that cultural relics can stabilise the subject matter of a film. Artifacts can stimulate curiosity and invoke nostalgia. Sign languages encode deeper information that ordinary speech may not properly convey in films. It concludes that the use of non-verbal codes in filmmaking deserves deep research into the culture portrayed and that non-verbal cues communicate deeper information about a people's cosmos in a film than dialogue. It recommends that adequate research should be made before producing a film and the idea of cultural values represented in artifacts and sign language should be ingrained in the courses for students to acquaint themselves with.

Keywords: cultural artifact, Nollywood films, non-verbal communication, sign language

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Authors: Saad M. Darwish, Magda M. Madbouly, Murad B. Khorsheed

Abstract:

Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper, several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD). SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi attribute hand gesture data to feature vectors. SVD optimally exposes the geometric structure of a matrix. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additive constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 86.21% for cluttered hand images.

Keywords: hand gesture recognition, hand detection, type-2 fuzzy logic, hidden Markov Model

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Authors: Paula Escudeiro, Nuno Escudeiro, Márcia Campos, Francisca Escudeiro

Abstract:

In today's world, technical and scientific knowledge plays a vital role in education, research, and employment. Deaf students face unique challenges in educational settings, particularly when it comes to understanding technical and scientific terminology. The reliance on written and spoken languages can create barriers for deaf individuals who primarily communicate using sign language. This lack of accessibility can hinder their learning experience and compromise equity in education. To address this issue, the TechWhiz project has been developed as a comprehensive glossary of scientific and technical concepts explained in sign language. By providing deaf students with access to education in their first language, TechWhiz aims to enhance their learning achievements and promote inclusivity while also fostering equity in education for all students.

Keywords: deaf students, technical and scientific knowledge, automatic sign language, inclusive education

Procedia PDF Downloads 68