Real-time Language Research Articles

Emerging Roots: Investigating Early Access to Meaning in Maltese Auditory Word Recognition.

In Semitic languages, the consonantal root is central to morphology, linking form and meaning. While psycholinguistic studies highlight its importance in language processing, the role of meaning in early lexical access and its representation remain unclear. This study investigates when meaning becomes accessible during the processing of Maltese verb forms, using a computational model based on the Discriminative Lexicon framework. Our model effectively comprehends and produces Maltese verbs, while also predicting response times in a masked auditory priming experiment. Results show that meaning is accessible early in lexical access and becomes more prominent after the target word is fully processed. This suggests that semantic information plays a critical role from the initial stages of lexical access, refining our understanding of real-time language comprehension. Our findings contribute to theories of lexical access and offer valuable insights for designing priming studies in psycholinguistics. Additionally, this study demonstrates the potential of computational models in investigating the relationship between form and meaning in languageprocessing.

Transforming Libraries: The Impact of Artificial Intelligence

Artificial intelligence (AI) is revolutionising library services and enhancing user experience, creating a new era of efficiency, accessibility, and innovation. This editorial explores how AI reshapes the library landscape, ushering in a new generation of efficiency, accessibility, and innovation. The AI’s most notable contribution to libraries is the automation of routine tasks, cataloguing and organising; librarians can now rely on AI algorithms to streamline. It saves librarians valuable time engaging in intellectually stimulating activities and ensures a more accurate and organised library system. AI-driven systems can efficiently analyse vast amounts of data, leading to improved search functionalities and more seamless information retrieval for library patrons. Moreover, AI is instrumental in the personalisation of library services. Through machine learning algorithms, libraries can analyse user behaviour, preferences, and historical data to provide customised recommendations. This enhances the user experience by offering tailored suggestions for reading materials, resources, and services, making libraries more relevant and engaging for diverse user groups. Another essential role of AI in libraries is the development of virtual assistants. These digital assistants, powered by natural language processing and machine learning, can help library patrons navigate the catalogue system, answer queries, and even provide Realtime language translation service. Virtual assistants help libraries become more inclusive and accessible, reaching more audiences with diverse language backgrounds and information needs.

Real-time Transcription Language Translation using Webrtc

This literature survey paper titled "Real-Time Transcription Language Translation Using WebRTC" explores the current landscape of real-time speech translation technologies. We summarize and analyze various research efforts in the domain, highlighting their methodologies, findings, and limitations. Additionally, we propose a novel approach that leverages WebRTC to enhance real-time transcription and translation efficiency. By identifying gaps in existing studies, we outline the scope for future research, aiming to improve user experience in multilingual communication. This survey serves as a comprehensive resource for researchers and practitioners interested in advancing real-time language translation technologies

Chunking in the Second Language: Implications for Language Learning and Teaching

Among the various challenges that adult and other late language learners face on their journey to achieving nativelike proficiency, chunking has been identified as one of the most difficult tasks to master. Language users are able to derive and utilize chunks during language processing—both in the first (L1) and the second language (L2)—yet the extent to which the L2 learners utilize and benefit from chunking is not on a par with L1 speakers. L2 learners are generally less sensitive to the statistical regularities in the linguistic input and possess a smaller repertoire of multiword expressions, leaving them susceptible to slower real-time language processing, hampered comprehension during conversation, and distinct production errors. Drawing on insights from Brian MacWhinney's Unified Competition Model of L1 and L2 acquisition, this review examines these unique challenges in L2 chunking as a function of differences between L1 and L2 learning. According to the Unified Competition Model, the existence of deeply entrenched L1 linguistic representations may hinder effective L2 chunking by encouraging over-segmentation in favor of L1 transference at the lexical level and diverting the necessary attention away from the grammatical elements in prefabricated multiword units. Based on these observations, we offer practical suggestions for educators to facilitate chunking in L2 learners and bring them closer to nativelike fluency.

Synchronized Shared Memory and Black-box Procedural Abstraction: Toward a Formal Semantics of Blech

Traditional imperative synchronous programming languages heavily rely on a strict separation between data memory and communication signals. Signals can be shared between computational units but cannot be overwritten within a synchronous reaction cycle. Memory can be destructively updated but cannot be shared between concurrent threads. This incoherence makes traditional imperative synchronous languages cumbersome for the programmer. The recent definition of sequentially constructive synchronous languages offers an improvement. It removes the separation between data memory and communication signals and unifies both through the notion of clock synchronized shared memory . However, it still depends on global causality analyses, which precludes black-box procedural abstraction. This complicates reuse and composition of software components. This article shows how black-box procedural abstraction can be accommodated inside the sequentially constructive model of computation. We present the Sequentially Constructive Procedural Language ( SCoPL ) and its semantic theory of policy-constructive synchronous processes. SCoPL supports black-box procedural abstractions using policy interfaces to ensure that procedure calls are memory-safe and wait-free and their scheduling is determinate and causal. At the same time, a policy interface constrains the level of freedom for the implementation and subsequent refactoring of a procedure. As a result, policies enable separate compilation and composition of procedures. We present our extensions abstractly as a formal semantics for SCoPL and motivate it concretely in the context of the open-source, embedded, real-time language Blech .

Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM.

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

Novel Wearable System to Recognize Sign Language in Real Time.

The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.

Exploiting beam search confidence for energy-efficient speech recognition

With mobile and embedded devices getting more integrated in our daily lives, the focus is increasingly shifting toward human-friendly interfaces, making automatic speech recognition (ASR) a central player as the ideal means of interaction with machines. ASR is essential for many cognitive computing applications, such as speech-based assistants, dictation systems and real-time language translation. Consequently, interest in speech technology has grown in the last few years, with more systems being proposed and higher accuracy levels being achieved, even surpassing human accuracy. However, highly accurate ASR systems are computationally expensive, requiring on the order of billions of arithmetic operations to decode each second of audio, which conflicts with a growing interest in deploying ASR on edge devices. On these devices, efficient hardware acceleration is key for achieving acceptable performance. In this paper, we propose a technique to improve the energy efficiency and performance of ASR systems, focusing on low-power hardware for edge devices. We focus on optimizing the DNN-based acoustic model evaluation, as we have observed it to be the main bottleneck in popular ASR systems, by leveraging run-time information from the beam search. By doing so, we reduce energy and execution time of the acoustic model evaluation by 25.6 and 25.9 %, respectively, with negligible accuracy loss.

A New Method for Documenting Sign Language Productions in Schools

Purpose: There are well-established guidelines for the recording, transcription, and analysis of spontaneous oral language samples by researchers, educators, and speech pathologists. In contrast, there is presently no consensus regarding methods for the written documentation of sign language samples. The Handshape Analysis Recording Tool (HART) is an innovative method for documenting and analyzing word level samples of signed languages in real time. Fluent sign language users can document the expressive sign productions of children to gather data on sign use and accuracy. Method: The HART was developed to document children's productions in Australian Sign Language (Auslan) in a bilingual–bicultural educational program for the Deaf in Australia. This written method was piloted with a group of fluent signing Deaf educational staff in 2014–2016, then used in 2022–2023 with a group of fluent signing professionals to examine inter- and intrarater reliability when coding parameters of sign accuracy. Results: Interrater reliability measured by Gwet's Agreement Coefficient, was “good” to “very good” across the four phonological parameters that are components of every sign: location, movement, handshape, and orientation. Conclusions: The findings of this study indicate that the HART can be a reliable tool for coding the accuracy of location, orientation, movement, and handshape parameters of Auslan phonology when used by professionals fluent in Auslan. The HART can be utilized with any sign language to gather word level sign language samples in a written form and document the phonological accuracy of signed productions.

Action research: Increasing Turkish EFL Learners’ Speaking Self-Efficacy in English Online Sessions

The main purpose of this study is to investigate the critical role of students' speaking self-efficacy, motivation and engagement during English online sessions. Additionally, the current research aims to provide a fresh perspective on addressing challenges such as low self-efficacy, speaking anxiety, and low motivation among the students, which may hinder effective communication in the target language. This study provides a valuable starting point for language educators and researchers to explore the potential benefits of incorporating interaction with non-native speakers in language learning settings. The action research design employed in this study, guided by the identified sources of self-efficacy proposed by Bandura (1994). The research was successfully carried out in a Compulsory Preparatory Group in Listening & Speaking sessions, where students' proficiency level in English was pre-intermediate (B1). The primary goal of English as a Foreign Language (EFL) learners in compulsory prep classes is to achieve mastery of English language skills. This proficiency is crucial as it enables the students to effectively engage with the extensive number of texts they will encounter, write essays, and, most importantly, communicate orally and deliver presentations in the target language once they transition to their respective faculties. Five guest-speakers from Egypt, Korea, Australia, Iran and Ethiopia were invited to join the sessions. The research procedure incorporates focus group discussions, enabling active student participation and feedback provision on each guest speaker, thereby promoting the development of speaking self-efficacy and boosting students’ motivation. Through this approach, students were exposed to essential opportunities for real-time language performance. The participation of foreigners in these sessions had a positive impact on students' English speaking self-efficacy and their overall performance. The findings of this research support the idea that integrating foreign guest-speakers effectively address challenges such as speaking anxiety, low self-efficacy, and low motivation among students in English online sessions.

The effectiveness of using simulation in learning a foreign language

The aim of this study was to measure how the use of simulation contributes to the improvement of speaking skills and the enhancement of students' motivation to learn a foreign language. The methods of observation, testing, and expert evaluation were used to diagnose the criteria of foreign language communicative competence (motivational, cognitive, and behavioral). The quality of the manifestation of interaction was correlated with its development, and three levels of foreign language communicative competence among students were distinguished: low (reproductive), medium (reproductive-productive), and high (productive). The study results showed that the EG significantly increased the indicators for all foreign language communicative competence criteria. The comparison of the results of the two experimental stages revealed a higher increase in indicators of high and medium levels of foreign language communicative competence in the second stage (high level—by 10.1%, medium level—by 4.9%). It was determined that simulations contribute to the gradual development of all foreign language communicative competence criteria. The main advantages of simulations are that they provide an opportunity to reproduce various communicative situations, thereby contributing to improving speaking skills, understanding the language in real time, and adapting to the requirements of real communication. The prospect for further research is the study of the impact of different forms of simulation on the effectiveness of foreign language learning.

A Deep Dive into the Technology Driving Modern Chatbots: A Review

Abstract: Chatbots have always been an interesting topic for research based on the technology used to build them. A chatbot is an intelligent system which can bring out conversations with a human using humanly or natural language in real time. Many businesses now use online platforms to reach out to the customer inquiries, while many of them turn to chatbots for improving their customer service and increasing their productivity. Based on various research being done on chatbots, some important issues regarding them have changed over time. Therefore, it is necessary to review some of the principal technologies most liable to build highly independent and self-learning chatbots. This paper introduces a comprehensive discussion about the technologies with a focus on recent advancements. At first, this paper will provide an overview of chatbots and then focus on technologies used to build the chatbots. It will also grab your attention towards the in demand chatbots built in the recent years and how they differ with each other in terms of functionalities

Sign Language Detection using ML Technologies

Sign language recognition is a leap forward for helping deaf-mute people. In sign language, every sign has a meaning assigned to it, so that it becomes easy to understand and interpret by the people. The main primary objective of our project is to bridge the gap between the deaf and dumb people and ordinary people without the need of an intermediary translator. Gesture recognition is a computer based visual technique used to detect and identify the object in an image or video. An application of this gesture recognition technique involves translating the sign language into the American language which can be further understood and interpreted by normal people. Many researchers have proposed their systems for the implementation of the ASL system. This report is a review of some studies related to the same topic. We addressed different approaches that used Convolutional Neural Networks (CNNs), K- Nearest Neighbours algorithm (KNN), Edge detection algorithm, Deep Neural Network (DNN), fuzzy clustering machine learning algorithm, Kernelized correlation filters (KCF) algorithm along with their results and drawbacks. Furthermore, we report on research gaps while summarizing these studies. From the above research papers, we got an average accuracy of 90.24%. To overcome the drawbacks, we propose a system which collects images for deep learning using webcam and OpenCV, with the help of TensorFlow Object Detection and Python that allows you to translate sign language in real time

Language Translator App

Language barriers have long posed challenges in effective communication across borders. To address this issue, the Language Translator app leverages Firebase ML Kit and Android Studio to create an efficient solution for real-time language translation. Unlike traditional methods, which require proficiency in both source and target languages, our app simplifies the process, enabling seamless communication regardless of linguistic differences. By optimizing the translation process through streamlined code, the app aims to enhance user experience and promote stress-free communication. This research paper explores the development and evaluation of the Language Translator app, assessing its effectiveness in facilitating everyday conversations and easing the learning curve for language acquisition.

Design and Implementation of Chinese Language Teaching System Based on Virtual Reality Technology

Traditional Chinese language teaching methods have always had problems, such as a lack of practical opportunities and interest and an inability to provide sufficient language environment and context. Virtual reality technology provides new possibilities to solve these problems. Through in-depth research and analysis, this article designs and develops a Chinese language teaching system based on virtual reality technology. This system utilizes virtual reality technology to create a simulated 3D Chinese language environment, enabling learners to experience interactive experiences firsthand. The system includes speech recognition, natural language processing, and artificial intelligence, enabling real-time language communication and student interaction. This system s design considers students learning needs and interests, allowing them to practice their Chinese application skills in real-world simulated environments. This system has broad prospects in Chinese language education and provides new research and innovation directions for educational institutions and developers.

Spontaneous perspective-taking in real-time language comprehension: evidence from eye-movements and grain of coordination

Linguistic communication requires interlocutors to consider differences in each other’s knowledge (perspective-taking). However, perspective-taking might either be spontaneous or strategic. We monitored listeners’ eye movements in a referential communication task. A virtual speaker gave temporally ambiguous instructions with scalar adjectives (“big” in “big cubic block”). Scalar adjectives assume a contrasting object (a small cubic block). We manipulated whether the contrasting object (a small triangle) for a competitor object (a big triangle) was in common ground (visible to both speaker and listener) or was occluded so it was in the listener’s privileged ground, in which case perspective-taking would allow earlier reference-resolution. We used a complex visual context with multiple objects, making strategic perspective-taking unlikely when all objects are in the listener’s referential domain. A turn-taking, puzzle-solving task manipulated whether participants could anticipate a more restricted referential domain. Pieces were either confined to a small area (requiring fine-grained coordination) or distributed across spatially distinct regions (requiring only coarse-grained coordination). Results strongly supported spontaneous perspective-taking: Although comprehension was less time-locked in the coarse-grained condition, participants in both conditions used perspective information to identify the target referent earlier when the competitor contrast was in privileged ground, even when participants believed instructions were computer-generated.

Enhancing Higher Vocational English Teaching through Edge Computing: A Framework for Real-Time Language Learning

Enhancing Higher Vocational English Teaching through Edge Computing: A Framework for Real-Time Language Learning

Children extract a new linguistic rule more quickly than adults.

Children achieve better long-term language outcomes than adults. However, it remains unclear whether children actually learn language more quickly than adults during real-time exposure to input-indicative of true superior language learning abilities-or whether this advantage stems from other factors. To examine this issue, we compared the rate at which children (8-10 years) and adults extracted a novel, hidden linguistic rule, in which novel articles probabilistically predicted the animacy of associated nouns (e.g., "gi lion"). Participants categorized these two-word phrases according to a second, explicitly instructed rule over two sessions, separated by an overnight delay. Both children and adults successfully learned the hidden animacy rule through mere exposure to the phrases, showing slower response times and decreased accuracy to occasional phrases that violated the rule. Critically, sensitivity to the hidden rule emerged much more quickly in children than adults; children showed a processing cost for violation trials from very early on in learning, whereas adults did not show reliable sensitivity to the rule until the second session. Children also showed superior generalization of the hidden animacy rule when asked to classify nonword trials (e.g., "gi badupi") according to the hidden animacy rule. Children and adults showed similar retention of the hidden rule over the delay period. These results provide insight into the nature of the critical period for language, suggesting that children have a true advantage over adults in the rate of implicit language learning. Relative to adults, children more rapidly extract hidden linguistic structures during real-time language exposure. RESEARCH HIGHLIGHTS: Children and adults both succeeded in implicitly learning a novel, uninstructed linguistic rule, based solely on exposure to input. Children learned the novel linguistic rules much more quickly than adults. Children showed better generalization performance than adults when asked to apply the novel rule to nonsense words without semantic content. Results provide insight into the nature of critical period effects in language, indicating that children have an advantage over adults in real-time language learning.

Applying LSTM and GRU Methods to Recognize and Interpret Hand Gestures, Poses, and Face-Based Sign Language in Real Time

This research uses a real-time, human-computer interaction application to examine sign language recognition. This work develops a rule-based hand gesture approach for Indonesian sign language in order to interpret some words using a combination of hand movements, mimics, and poses. The main objective in this study is the recognition of sign language that is based on hand movements made in front of the body with one or two hands, movements which may involve switching between the left and right hand or may be combined with mimics and poses. To overcome this problem, a research framework is developed by coordinating hand gestures with poses and mimics to create features by using holistic MediaPipe. To train and test data in real time, the long short time memory (LSTM) and gated recurrent unit (GRU) approaches are used. The research findings presented in this paper show that hand gestures in real-time interactions are reliably recognized, and some words are interpreted with the high accuracy rates of 94% and 96% for the LSTM and GRU methods, respectively.

Large-scale evidence for logarithmic effects of word predictability on reading time

During real-time language comprehension, our minds rapidly decode complex meanings from sequences of words. The difficulty of doing so is known to be related to words' contextual predictability, but what cognitive processes do these predictability effects reflect? In one view, predictability effects reflect facilitation due to anticipatory processing of words that are predictable from context. This view predicts a linear effect of predictability on processing demand. In another view, predictability effects reflect the costs of probabilistic inference over sentence interpretations. This view predicts either a logarithmic or a superlogarithmic effect of predictability on processing demand, depending on whether it assumes pressures toward a uniform distribution of information over time. The empirical record is currently mixed. Here, we revisit this question at scale: We analyze six reading datasets, estimate next-word probabilities with diverse statistical language models, and model reading times using recent advances in nonlinear regression. Results support a logarithmic effect of word predictability on processing difficulty, which favors probabilistic inference as a key component of human language processing.