Language Interpretation Research Articles

Reverse Sign Language Recognition System Using Machine Learning

Deaf and hard-of-hearing people communicate with each other and their society by using sign language. As this is a very natural method for pupils to stay in touch with computers, many academics are working on it to make it less complex and more convenient for use. So the main objective of gesture recognition research is to make systems which can interact and communicate while understanding human gestures and use them. In simple words to communicate information. Fast and extremely accurate hand detection and realtime hand gestures. Identification should be possible with vision-based hand gesture circumstances and interfaces. Learning and knowing sign movements and gestures is the kick start in making words and sentences for computer assisted sign language interpretation. Both dynamic and static sign actions are open and available. Both ways of gesture recognition are crucial to human culture, even if static gesture recognition is much easier than dynamic gesture recognition. When a human enters the value of alphabets and numerical value as input, the system immediately displays or outputs the appropriate recognised character shows the gesture on the monitor screen. In the following, research projects that have led to a proper system that uses convolutional neural networks to identify handwriting on the basis of the depth pictures and Hand Languages (Brain lipi) the collects. Keywords- Convolutional neural network,Text recognition ,Convert to image,StackGAN,simultaneous Tracking text and converting to image, gesture recognition,display output as sign gesture,training machine to(A-z to 1-0).

Dynamic gesture recognition using hand pose-based neural networks for sign language interpretation

Dynamic gesture recognition using hand pose-based neural networks for sign language interpretation

”Kieli oli tässä kohtaa vain väline” – kulttuuritulkkien, opettajien ja kuraattoreiden näkemyksiä kielestä ja kulttuuritulkkien roolista

Alongside public service interpreting consisting of language interpretation, many institutions, including institutions of learning, increasingly use intercultural mediators to communicate with migrants. Drawing on the research tradition of sociology of translation, we analyze our data from two perspectives, mobilizing the sociological concepts of role, agency, and (affective) facework, as well as the linguistic anthropological concept of language ideology. The data consist of semi-structured thematic interviews with intercultural mediators and teachers and school social workers who work with them. The analysis shows that intercultural mediators’ affective labor, based on their broad agency, is fundamental in building trust between migrant families and the school. Intercultural mediation is different from public service interpreting, where monolithic, referential language ideologies are often felt to prevent the achievement of mutual understanding between the participants.

DFCNet +: Cross-modal dynamic feature contrast net for continuous sign language recognition

In sign language communication, the combination of hand signs and facial expressions is used to convey messages in a fluid manner. Accurate interpretation relies heavily on understanding the context of these signs. Current methods, however, often focus on static images, missing the continuous flow and the story that unfolds through successive movements in sign language. To address this constraint, our research introduces the Dynamic Feature Contrast Net Plus (DFCNet+), a novel model that incorporates both dynamic feature extraction and cross-modal learning. The dynamic feature extraction module of DFCNet+ uses dynamic trajectory capture to monitor and record motion across frames and apply key features as an enhancement tool that highlights pixels that are critical for recognizing important sign language movements, allowing the model to follow the temporal variation of the signs. In the cross-modal learning module, we depart from the conventional approach of aligning video frames with textual descriptions. Instead, we adopt a gloss-level alignment, which provides a more detailed match between the visual signals and their corresponding text glosses, capturing the intricate relationship between what is seen and the associated text. The enhanced proficiency of DFCNet+ in discerning inter-frame details translates to heightened precision on benchmarks such as PHOENIX14, PHOENIX14-T and CSL-Daily. Such performance underscores its advantage in dynamic feature capture and inter-modal learning compared to conventional approaches to sign language interpretation. Our code is available at https://github.com/fyzjut/DFCNet_Plus.

Examining the association between professional language interpreter services and intensive care unit utilization among patients with non-English language preference: Evidence from a large U.S. hospital

ObjectiveTo examine the healthcare utilization of patients with non-English language preference (NELP) who utilized a professional language interpreter (PLI) in the intensive care unit (ICU) compared to similar patients with NELP who did not utilize a PLI in the ICU. MethodsSingle center cohort study of patients with NELP with at least one ICU admission a large academic medical center in the U.S. Midwest (1/1/2008-12/31/2022). The first model examined ICU length-of-stay (LOS) using a negative binomial and the second model examined whether a patient was readmitted to the ICU using a logistic regression with each model controlling for PLI utilization and covariates. ResultsPatients with NELP who utilized a PLI in the ICU had 0.87-days longer in the ICU (p<0.01) and had a 46% decreased odds of being readmitted to the ICU (p<0.01) than a comparable patient with NELP who did not utilize a PLI in the ICU. ConclusionProviding patients with NELP with access to a PLI in the ICU can improve patient outcomes and reduce language barriers. Practice implicationsThese results can provide the justification to potentially increase PLI staffing levels or increase the access to existing PLIs for more patients with NELP in ICUs.

Innovative Hand Sign to Text-and-Speech Conversion System

In present scenario, it is difficult for people who are unable to speak due to various reasons. It may not be a permanent condition, as muteness and deafness can be caused or manifest due to several different phenomena, such as physiological injury, illness, medical side effects, psychological trauma, developmental disorders, or neurological disorders. People who are suffering from this typically use the Sign Language to communicate with other people. However, it is not possible for everyone to be acquainted with communication using sign language. This project focuses on developing a solution which can be used by regular people to understand the people having mutism. Hand Sign Recognition Systems have evolved significantly with advancements in machine learning and deep learning techniques. These systems are designed to recognize gestures or signs, making them useful in various applications such as sign language interpretation, human-computer interaction, and virtual reality. This paper gives an overview of the current state of hand sign recognition systems, highlighting the techniques used, challenges, and possible future directions. The focus of the paper is on deep learning-based methods and their impact on the accuracy and usability of hand sign recognition in real-time applications [1]

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

The Development of AI-Powered Automatic Video Sign Language Translation Systems

People who are deaf or hard of hearing often face challenges in fully understanding written subtitles in videos due to the differences between sign language and spoken language, each having unique grammar and structures. While many social media platforms provide automatic subtitles, they are often insufficient for accessibility. Sign language interpretation should be included to make video content fully accessible. Traditionally, adding sign language to videos involves a time-consuming process of recording and embedding separate translation videos, which must be redone with any changes to the original video. The plugin simplifies this process by dynamically translating updates directly from subtitle files, significantly reducing the time, effort, and cost involved. It allows seamless sign language support for any video without modifying the original content. The plugin integrates with video players, providing a user-controlled, customizable sign language window that can be activated, moved, resized, or turned off as desired.

Workshops: Enhancing communication for nursing students with patients with hearing disabilities

Abstract Background Nursing students need guidance on effective communication strategies for interacting with patients with hearing disabilities, emphasizing the importance of addressing this gap in their education as future healthcare professionals. Methods A cross-sectional survey was conducted among nursing students at seven institutions before and after workshops led by guides with hearing disabilities from a non-profit organization Ma’agalei Shema and funded by the National Insurance Institute. Data was collected from 2023 to March 2024 and analyzed using SPSS software. Results 279 participants completed the survey before the workshops. and 136 responding afterward. Almost 80% were female. Before the workshops, 68.46% of students expected to gain communication tools for patients with hearing disabilities. About one-third believed the healthcare system offers accessible treatment to these patients, while only 22.22% foresaw no barriers. One-third reported no clinical contact with such patients. Among those who did, lip-reading (25.53%), writing (17.73%), assistance from family members (15.6%), and sign language (9.22%) were commonly used. Only 2.84% used professional sign language interpreters, and 20.57% reported never meeting a patient with hearing disabilities (8% reported “other”). Post-workshop surveys showed that around 82.48% of participants felt the workshops would significantly enhance their communication skills with such patients, with only 5.11% reporting a lack of practical tools received. Conclusions Practical workshops can significantly enhance communication skills and improve patient care. This study underscores the potential impact of minor adjustments in communication strategies on patient care, such as increasing the use of professional interpreters. Main messages: Providing training to nursing students in effective communication with these patients can streamline interactions, diminish reliance on family intermediaries, and enhance medical care. Key messages • Nursing students need guidance on communicating with patients with hearing disabilities to improve future healthcare interactions. • Practical workshops enhance communication skills, stressing the importance of minor adjustments for better patient care.

26 Culturally informed developmental paediatric care: A qualitative study

Abstract Background Paediatricians provide developmental care through developmental surveillance, identification and management of specific developmental disorders, and health advocacy. Despite being one of the most common reasons to visit a paediatrician, the provision of care in this field is challenging as it intersects with medical, mental health, and educational systems. Additionally, the rise of international migration, inequities in health systems, and unique culturally based concepts of child development require paediatricians to have unique skills for culturally informed care provision in this area. Objectives A qualitative study was conducted to describe the perceptions of paediatricians on providing culturally informed developmental paediatric care to diverse populations. Design/Methods Semi-structured interviews were completed with paediatricians who primarily provide developmental care to understand the challenges, opportunities, and potential educational enhancements to improve culturally informed care for developmental paediatrics. Thematic analysis was used to analyze the transcribed interviews using inductive and deductive approaches. Results Three major themes emerged from 18 individual semi-structured interviews. The first theme, “cultural translation,” described language differences, interpretation, and trust-building between discordant physician/family dyads. The second theme, “inequity,” described systemic barriers that diverse populations experience, power distance in healthcare relationships, and pandemic-related amplifications of inequity. The third theme, “educational opportunities,” highlighted reflective practice opportunities, immersive experiences in diverse settings, and building a community of practice for paediatricians to build their knowledge base. Paediatricians practicing developmental paediatric care highlighted unique elements to the practice which may benefit from practice changes and curricular reform. Integration of meaningful training in the use of interpretation services, multicultural health brokers, and care coordination teams is a recommended intervention which is transferable to many disciplines. Peer-mentorship from colleagues of differing cultural backgrounds may help reduce race-based stereotypes and enhance perspective taking. Conclusion Paediatricians had a variety of perspectives on the provision of developmental paediatric care to diverse populations within Canada. There were a range of perceived opportunities for improvements in care noted on individual and systems levels. The development of infrastructure to build capacity in culturally informed developmental paediatric care to reduce culturally based health gaps is a needed future direction for developmental paediatrics.

Occupational Safety, Health, and Well-Being Concerns and Solutions for Management Reported by Sign Language Interpreters: A Qualitative Study

While the occupational health of sign language interpreters has traditionally focused on physical health, evidence demonstrating mental health concerns is growing and supporting a shift to a more integrated approach. We embarked on a qualitative study to guide the adaptation of a previously developed Total Worker Health® program to the context of sign language interpreting. Eight unstructured 90-min focus groups were conducted. Interpreters reported occupational safety, health, and well-being concerns and shared their solutions for management. Twenty-seven interpreters participated (aged 53.7 years; 81% female; 85% white). Predominant concerns centered on topics like workplace violence, secondary traumatic stress or vicarious trauma, lack of work–life integration or boundaries, and loss of agency or loss of self. The organizational culture of the field fostered deprioritization of self, oppression, elitism, sexism, and unhealthy relationships with interpreter peers and community members. Physical health remained a contributor, specifically the physical effects of non-physical work, aging, and differences in exposures across interpreting settings but paled in comparison to mental health and organizational culture. Solutions for management included but were not limited to prioritization of jobs, creating safe spaces/communities of supported practice, and exercise. This study supports the evolution of future research, practice, policy, and capacity building aspiring to a more integrated approach.

82 Assessing the usage of and barriers to using language interpretation services in a Canadian tertiary paediatric center

Abstract Background Canada has one of the highest immigration rates worldwide per population with roughly 447,180 permanent residents welcomed in 2022. Limited-English proficiency (LEP) populations face barriers in accessing health care with implications including delayed presentation, misdiagnosis, poor compliance, decreased patient comprehension, and adverse events. Adequate health care provider training in using language interpretation services (LIS) is crucial to improve the quality of patient care and reduce miscommunication. There is a lack of research in this area in Canadian paediatric populations. Objectives To identify attitudes towards and usage of LIS among health care providers in a tertiary paediatric center, as well as levels of LIS training received. Design/Methods An online survey was developed and distributed to paediatric health care professionals including physicians, residents/fellows, nursing staff, and allied health providers. Survey completion was anonymous and voluntary; data collection occurred from October 2022–February 2023. Descriptive statistics were used for quantitative data and thematic analysis for free text responses. Results 345 respondents accessed the survey and 281(81%) completed it. Multiple paediatric professions were surveyed with most responses stemming from nursing staff, residents and physicians, and other allied health. 52.8% of respondents encountered a paediatric patient and/or their caregivers with a language barrier in English once per week or more. LIS were used by 53.1% of respondents once per week or more. 51.5% of respondents reported no previous training in using LIS in patient care, and 3.6% reported previous formal training. 57.9% of respondents felt LIS training was very beneficial to their role and 53.7% reported this increased their LIS usage. Overall, in-person interpreters were perceived as the most effective LIS modality followed by video and telephone interpreters. Barriers to using LIS included: inability to access desired modality and/or language; insufficient time; and lack of comfort in effective usage. Conclusion It was evident respondents felt LIS was integral to patient care, especially with an increasingly diverse population. Multiple barriers to using LIS were identified with likely applicability to other Canadian paediatric facilities. Suggested improvements were themed around improving LIS accessibility, improving LIS education and awareness, and increasing access to rare languages. This study has important implications for LIS and paediatric care, including informing educational and advocacy initiatives, policy change, and LIS resource allocation.

Accessibility in an Inclusive Academic Environment: The Case of Takoradi Technical University

This study examines the extent to which Takoradi Technical University (TTU) in Ghana has achieved an inclusive academic environment for students with disabilities. Despite Ghana's commitment to inclusive education and various policies supporting persons with disabilities (PWDs), there remains a significant research gap regarding the practical implementation and effectiveness of these policies at the university level. This research addresses this gap by evaluating TTU's accessibility measures and support services. The study employs a phenomenological approach to explore the lived experiences of students with disabilities, faculty, and staff at TTU. It investigates the alignment of TTU’s practices with national and international inclusive education policies, such as the Persons with Disability Act (715) and the United Nations Convention on the Rights of Persons with Disabilities. The research focuses on three main objectives: identifying the teaching and learning needs of disabled students, assessing the accessibility of TTU's built environment, and evaluating the satisfaction of students with the available support services. Data were collected through qualitative interviews with 28 participants, including students with disabilities, lecturers, and support staff. Findings reveal that TTU has established various support services, including sign language interpretation, e-learning support, and assistive devices. However, students reported inadequacies in these services, citing needs for additional support such as braille services and extended examination time. Furthermore, the built environment at TTU was found to be largely inaccessible to students with physical disabilities, leading to widespread dissatisfaction among the student body. The study highlights that while TTU has made commendable efforts in providing institutionalized support for disabled students, significant gaps remain in service adequacy and physical accessibility. The findings underscore the need for enhanced strategies to improve inclusivity at TTU and similar institutions, aligning practical implementations with policy frameworks to better serve students with disabilities

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

EPS mid-career prize: An integrated framework for the learning, recognition and interpretation of words.

In this article, I review the evidence on the involvement of sleep and consolidation in word learning and processing during language comprehension, focusing on implications for theory. The theoretical basis for the review is a complementary systems account of word learning involving flexible (hippocampal) and stable (cortical) pathways to lexical knowledge. I argue that the accumulated data are consistent with a role for both pathways in both learning and recognition of lexical items, with sleep and consolidation supporting the transfer of recent experience between the pathways. The level of involvement of each pathway is dependent on key factors, such as consistency with prior knowledge in the case of learning, and reliance on context and/or automaticity in the case of recognition. As a consequence, the notion of a mental lexicon cannot really be restricted to just the listener's stable knowledge about words: flexible knowledge and recent experiences are also important. Furthermore, I argue that the flexible pathway plays a critical role even in the absence of new lexical items. The available evidence suggests that this pathway encodes (and potentially consolidates) recent linguistic experiences, providing potential benefits to interpretation of subsequent language and the long-term retention of knowledge. In conclusion, I propose that a dual-pathway account incorporating both flexibility and stability is necessary to explain the learning, recognition, and interpretation of words.

Synthesizing Formal Semantics from Executable Interpreters

Program verification and synthesis frameworks that allow one to customize the language in which one is interested typically require the user to provide a formally defined semantics for the language. Because writing a formal semantics can be a daunting and error-prone task, this requirement stands in the way of such frameworks being adopted by non-expert users. We present an algorithm that can automatically synthesize inductively defined syntax-directed semantics when given (i) a grammar describing the syntax of a language and (ii) an executable (closed-box) interpreter for computing the semantics of programs in the language of the grammar. Our algorithm synthesizes the semantics in the form of Constrained-Horn Clauses (CHCs), a natural, extensible, and formal logical framework for specifying inductively defined relations that has recently received widespread adoption in program verification and synthesis. The key innovation of our synthesis algorithm is a Counterexample-Guided Synthesis (CEGIS) approach that breaks the hard problem of synthesizing a set of constrained Horn clauses into small, tractable expression-synthesis problems that can be dispatched to existing SyGuS synthesizers. Our tool Synantic synthesized inductively-defined formal semantics from 14 interpreters for languages used in program-synthesis applications. When synthesizing formal semantics for one of our benchmarks, Synantic unveiled an inconsistency in the semantics computed by the interpreter for a language of regular expressions; fixing the inconsistency resulted in a more efficient semantics and, for some cases, in a 1.2x speedup for a synthesizer solving synthesis problems over such a language.

Opportunities and lessons learned from a retrospective analysis of administrative billing data to understand the language profile of high-risk close contacts of COVID-19 cases in Ontario.

During a public health emergency, it is vital to have access to data sources that can identify communities disproportionately affected and to ensure public health communications are meeting the needs of diverse populations. To explore how administrative billing data for language interpretation services could be used as an additional source of information to understand the language profile of high-risk close contacts of COVID-19 cases. A retrospective descriptive analysis was conducted using administrative billing data from Public Health Ontario's Contact Tracing Initiative from May 2020 to February 2022. Data from the Contact Tracing Initiative were utilized to identify drivers that could have influenced patterns in language interpretation requests. Trends were compared with community language profiles using 2021 Canadian Census data. Interpreters responded to 2,604 requests across 38,518 interpretation minutes and provided information in 50 different languages. The top five requested languages were French, Arabic, Spanish, Punjabi and Mandarin. Five distinct periods were identified of different language predominance including Spanish in spring/summer 2020, French in summer/fall 2020 and Arabic in spring 2021. Overall, these trends aligned with the language profile of health units contributing most submissions. Public health agencies could benefit from using existing secondary data sources to understand the language interpretation needs of their communities. This study also demonstrated how existing data sources could be used to help assess how communities are being disproportionately affected by public health emergencies and how this might change over time.

Theory of affective pragmatics under biolinguistics.

This paper introduces a pioneering investigation into affective pragmatics through the perspective of Darwinian Biolinguistics, an interdisciplinary field at the nexus of biological and linguistic principles. Anchored in Darwin's theory of evolution and the latest developments in neurobiology, this study delves into the influence of biological factors---especially those pertaining to the brain's emotional processing on pragmatic communication. The research posits that human emotional responses, inherent in our biological constitution, profoundly influence the usage and interpretation of language in social interactions.

A wearable sign language translation device utilizing silicone-hydrogel hybrid triboelectric sensor arrays and machine learning

Sign language is a crucial communication tool for the hearing impaired to interact with the outside world. However, the low prevalence of sign language leads to significant communication barriers between the hearing impaired and others. These barriers can be alleviated by using electronic sign language translation devices, but such devices typically face challenges related to their bulkiness, rigidity, and high cost. Herein, we present a cost-effective, flexible, and wearable device designed for the interpretation of sign language, leveraging machine learning algorithms to transform sign language movements into spoken language accurately. Our device is equipped with flexible and stretchable triboelectric sensor (FS-TS) arrays and a printed circuit board for efficient signal processing and wireless transmission. These FS-TSs are constructed from cost-effective materials including silicone, hydrogel, and fluorinated ethylene propylene (FEP) powders, which, while affordable, ensure rapid response and heightened sensitivity. By analyzing 1,000 sets of sign language gestures with machine learning, our system has achieved an impressive recognition accuracy of 98.5%. This achievement underscores the potential of our system as an economically viable and scalable solution for enhancing sign language recognition within the wearable electronics domain.

A survey on pragmatic processing techniques

Pragmatics, situated in the domains of linguistics and computational linguistics, explores the influence of context on language interpretation, extending beyond the literal meaning of expressions. It constitutes a fundamental element for natural language understanding in machine intelligence. With the advancement of large language models, the research focus in natural language processing has predominantly shifted toward high-level task processing, inadvertently downplaying the importance of foundational pragmatic processing tasks. Nevertheless, pragmatics serves as a crucial medium for unraveling human language cognition. The exploration of pragmatic processing stands as a pivotal facet in realizing linguistic intelligence. This survey encompasses important pragmatic processing techniques for subjective and emotive tasks, such as personality recognition, sarcasm detection, metaphor understanding, aspect extraction, and sentiment polarity detection. It spans theoretical research, the forefront of pragmatic processing techniques, and downstream applications, aiming to highlight the significance of these low-level tasks in advancing natural language understanding and linguistic intelligence.