Automatic Translation System Research Articles

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.

Construction of English corpus oral instant translation model based on information security and deep learning

In order to improve the quality of translation, avoid translation ambiguity and accurately present the content of the source language, supported by the concept of deep learning and guaranteed by information security, an instant oral translation model is constructed for English corpus. The aim of this study is to enhance the efficiency and accuracy of oral translation systems through the application of deep learning algorithms. Specifically, we employ a sample training mechanism tailored to the unique characteristics of oral translation, allowing for separate training of system interaction and translation data. Furthermore, by redesigning the interaction hardware, this research comprehensively redefines the hardware structure of the translation system, marking a significant step towards improving the usability and performance of such systems. After obtaining and processing effective security sensitive information, language resources are managed by using database management system, which fundamentally improves the level of network information security. The performance of the existing oral automatic translation system (Test Group 1) and the system designed in this paper (Test Group 2) is tested by experiments, and the results are as follows: (1) The translation system designed here has better interactive performance, and it is better than Test Group 1. (2) The adaptive index value of Test Group 1 is 1, and that of Test Group 2 is 0.5, which proves that the adaptive ability of system algorithm of Test Group 2 is better than that of Test Group 1. (3) When comparing the translation speed, the translation time of Test Group 2 is only 70.7 s, while that of Test Group 1 is 130.6 s, so the proposed translation system is obviously superior to that of Test Group 1.

Automatic News Summarization using Web Scraping

In an era of information overload, accessing relevant news content efficiently is crucial. However, language barriers often hinder users from accessing news articles in languages they understand. To address this challenge, we propose an automatic news summarization and translation system. This system aggregates news articles from multiple URLs, summarizes them into concise summaries, and translates the summaries into the user's preferred language. By leveraging web scraping, natural language processing, and translation integration techniques, our system aims to provide users with access to relevant news content in their preferred language, thereby overcoming language barriers and fostering global connectivity. Keywords— automatic news summarization, translation integration, web scraping, natural language processing, language barriers, information accessibility, global connectivity, user preferences, multilingual content, web-based news aggregation

Systematic Review of English/Arabic Machine Translation Postediting: Implications for AI Application in Translation Research and Pedagogy

The twenty-first century has witnessed an extensive evolution in translation practice thanks to the accelerated progress in machine translation tools and software. With the increased scalability and availability of machine translation software empowered by artificial intelligence, translation students and practitioners have continued to show an unwavering reliance on automatic translation systems. Academically, there is little recognition of the need to develop machine translation skillsets amongst translation learners in English/Arabic translation programs. This study provides a systematic review of machine translation postediting with reference to English/Arabic machine translation. Using the Preferred Reporting Items for Systematic Review and Meta-Analysis, the paper reviewed 60 studies conducted since the beginning of the twenty-first century and classified them by different metrics to identify relevant trends and research gaps. The results showed that research on the topic has been primarily prescriptive, concentrating on evaluating and developing machine translation software while neglecting aspects related to translators’ skillsets and competencies. The paper highlights the significance of postediting as an important digital literacy to be developed among Arabic translation students and the need to bridge the existing research and pedagogic gap in MT education.

A Performance Comparison of Japanese Sign Language Recognition with ViT and CNN Using Angular Features

In recent years, developments in deep learning technology have driven significant advancements in research aimed at facilitating communication with individuals who have hearing impairments. The focus has been on enhancing automatic recognition and translation systems for sign language. This study proposes a novel approach using a vision transformer (ViT) for recognizing Japanese Sign Language. Our method employs a pose estimation library, MediaPipe, to extract the positional coordinates of each finger joint within video frames and generate one-dimensional angular feature data from these coordinates. Then, the code arranges these feature data in a temporal sequence to form a two-dimensional input vector for the ViT model. To determine the optimal configuration, this study evaluated recognition accuracy by manipulating the number of encoder layers within the ViT model and compared against traditional convolutional neural network (CNN) models to evaluate its effectiveness. The experimental results showed 99.7% accuracy for the method using the ViT model and 99.3% for the results using the CNN. We demonstrated the efficacy of our approach through real-time recognition experiments using Japanese sign language videos.

Automatic sign language translation system using neural network technologies and 3D animation

Implementation of automatic sign language translation software in the process of social inclusion of people with hearing impairment is an important task. Social inclusion for people with hearing disabilities is an acute problem that must be solved in the context of the development of IT technologies and legislative initiatives that ensure the rights of people with disabilities and their equal opportunities. This substantiates the relevance of the research of assistive technologies, in the context of software tools, such as the process of social inclusion of people with severe hearing impairment in society. The subject of research is methods of automated sign language translation using intelligent technologies. The purpose of the work is the development and research of sign language automation methods to improve the quality of life of people with hearing impairments in accordance with the "Goals of Sustainable Development of Ukraine" (in the "Reduction of Inequality" part). The main tasks of the research are the development and testing of methods of converting sign language into text, converting text into sign language, as well as automating translation from one sign language to another sign language using modern intelligent technologies. Neural network modeling and 3D animation methods were used to solve these problems. The following results were obtained in the work: the main problems and tasks of social inclusion for people with hearing impairments were identified; a comparative analysis of modern methods and software platforms of automatic sign language translation was carried out; a system combining the SL-to-Text method is proposed and investigated; the Text-to-SL method using 3D animation to generate sign language concepts; the method of generating a 3D-animated gesture from video recordings; method of implementing the Sign Language1 to Sign Language2 technology. For gesture recognition, a convolutional neural network model is used, which is trained using imported and system-generated datasets of video gestures. The trained model has a high recognition accuracy (98.52%). The creation of a 3D model for displaying the gesture on the screen and its processing took place in the Unity 3D environment. The structure of the project, executive and auxiliary files used to build 3D animation for the generation of sign language concepts includes: event handler files; display results according to which they carry information about the position of the tracked points of the body; files that store the characteristics of materials that have been added to certain body mapping points. Conclusions: the proposed methods of automated translation have practical significance, which is confirmed by the demo versions of the software applications "Sign Language to Text" and "Text to Sign Language". A promising direction for continuing research on the topic of the work is the improvement of SL1-to-SL2 methods, the creation of open datasets of video gestures, the joining of scientists and developers to fill dictionaries with concepts of various sign languages.

Spatio-temporal representation learning enhanced speech emotion recognition with multi-head attention mechanisms

Speech emotion recognition (SER) systems have become essential in various fields, including intelligent healthcare, customer service, call centers, automatic translation systems, and human–computer interaction. However, current approaches predominantly rely on single frame-level or utterance-level features, offering only shallow or deep characterization, and fail to fully exploit the diverse types, levels, and scales of emotion features. The limited ability of single features to capture speech emotion information, along with the ineffective combination of different features’ complementary advantages through simple fusion, pose significant challenges. To address these issues, this paper presents a novel spatio-temporal representation learning enhanced speech emotion recognition with multi-head attention mechanisms(STRL-SER). The proposed technique integrates fine-grained frame-level features and coarse-grained utterance-level emotion features, while employing separate modules to extract deep representations at different levels. In the frame-level module, we introduce parallel networks and utilize a bidirectional long short-term memory network (BiLSTM) and an attention-based multi-scale convolutional neural network (CNN) to capture the spatio-temporal representation details of diverse frame-level signals. Consequently, we extract deep representations of utterance-level features to effectively learn global speech emotion features. To leverage the advantages of different feature types, we introduce a multi-head attention mechanism that fuses the deep representations from various levels. This fusion approach retains the distinctive qualities of each feature type. Finally, we employ segment-level multiplexed decision making to generate the ultimate classification results. We evaluate the effectiveness of our proposed method on two widely recognized benchmark datasets: IEMOCAP and RAVDESS. The results demonstrate that our method achieves notable performance improvements compared to previous studies. On the IEMOCAP dataset, our method achieves a weighted accuracy (WA) of 81.60% and an unweighted accuracy (UA) of 79.32%. Similarly, on the RAVDESS dataset, we achieve a WA of 88.88% and a UA of 87.85%. These outcomes confirm the substantial advancements realized by our proposed method.

Retracted: Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things

Retracted: Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things

Cross-lingual Text Reuse Detection at Document Level for English-Urdu Language Pair

In recent years, the problem of Cross-Lingual Text Reuse Detection (CLTRD) has gained the interest of the research community due to the availability of large digital repositories and automatic Machine Translation (MT) systems. These systems are readily available and openly accessible, which makes it easier to reuse text across languages but hard to detect. In previous studies, different corpora and methods have been developed for CLTRD at the sentence/passage level for the English-Urdu language pair. However, there is a lack of large standard corpora and methods for CLTRD for the English-Urdu language pair at the document level. To overcome this limitation, the significant contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus, called the TREU (Text Reuse for English-Urdu) corpus. It contains English to Urdu real cases of text reuse at the document level. The corpus is manually labelled into three categories (Wholly Derived = 672, Partially Derived = 888, and Non Derived = 697) with the source text in English and the derived text in the Urdu language. Another contribution of this study is the evaluation of the TREU corpus using a diversified range of methods to show its usefulness and how it can be utilized in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse at the document level. The best evaluation results, for both binary ( F 1 = 0.78) and ternary ( F 1 = 0.66) classification tasks, are obtained using a combination of all Translation plus Mono-lingual Analysis (T+MA) based methods. The TREU corpus is publicly available to promote CLTRD research in an under-resourced language, i.e., Urdu.

English–Assamese neural machine translation using prior alignment and pre-trained language model

In a multilingual country like India, automatic natural language translation plays a key role in building a community with different linguistic people. Many researchers have explored and improved the translation process for high-resource languages such as English, German, etc., and achieved state-of-the-art results. However, the unavailability of adequate data is the prime obstacle to automatic natural language translation of low-resource north-eastern Indian languages such as Mizo, Khasi, and Assamese. Though the recent past has witnessed a deluge in several automatic natural language translation systems for low-resource languages, the low values of their evaluation measures indicate the scope for improvement. In the recent past, the neural machine translation approach has significantly improved translation quality, and the credit goes to the availability of a huge amount of data. Subsequently, the neural machine translation approach for low-resource language is underrepresented due to the unavailability of adequate data. In this work, we have considered a low-resource English–Assamese pair using the transformer-based neural machine translation, which leverages the use of prior alignment and a pre-trained language model. To extract alignment information from the source–target sentences, we have used the pre-trained multilingual contextual embeddings-based alignment technique. Also, the transformer-based language model is built using monolingual target sentences. With the use of both prior alignment and a pre-trained language model, the transformer-based neural machine translation model shows improvement, and we have achieved state-of-the-art results for the English-to-Assamese and Assamese-to-English translation, respectively.

Automatic Machine Translation System with Artificial Intelligence Theory

At present, with the continuous development of computer technology, automatic machine translation system has been adopted by many universities and research institutes, and it has entered the stage of practical and commercialization. In recent years, with the continuous development of artificial intelligence technology, a lot of research has been conducted on it. Based on this current situation, this paper conducts a research and analysis on the automatic machine translation system of artificial intelligence theory.

Online translated system: DESIGN AND Implementation

In the presence of technology and taking into account the concept of globalization, the application of chat is one of the most important aspects of our daily life to use different purposes that meet the needs of education, business and personal communication where the barrier is the language in most cases. Although we can send text using the chat app but there is still no known chat app where people can chat in different languages with the help of automatic translation system. Here the author decided to develop a mobile application with a chat and translation system in which users can chat in their preferred language for the translation system. Businessmen will help to facilitate communication with their foreign partners and students can talk to their friends abroad. It is useful to learn different languages and is useful to communicate with each other. Users only need to select the language they want to chat from the specified system. The system will use the Microsoft Translator API to translate in different languages.

Russian Sign Language Database for Clinical Use: Data and Annotation Peculiarities

This work has been carried out by the members of the Laboratory of Speech and Multimodal Interfaces of the St. Petersburg Federal Research Center of the Russian Academy of Sciences within an interdisciplinary research project aimed at the creation of an automatic Russian sign language translation system. The paper presents the design of a Russian sign language digital database for a specific subject area, namely, «The first visit to doctor».Our database is meant to be used first of all a dataset for training neural network-based systems of automatic translation from Russian sign language. But also, it can be of interest for linguistics of sign languages in general since the approach elucidates solution of a number of distorting phenomena typical for continuous sign language tracking, such as epenthesis, assimilation, reduction, and hold deletion.The principal difference between the presented video data and other datasets developed for similar purposes is the use of continuous sign utterances and elements of Russian sign language proper instead of the so-called “signed Russian” (that is, the visual form of the Russian spoken language), popular in deaf schooling.One of the most challenging problems, dealt with in the paper, is an automatic segmentation of sign speech into separate meaningful units with clearly defined boundaries. Its solution is hampered by signs’ deformations in continuous speech. In this paper, the segmentation is carried out within the framework of the movement-hold model. This approach allows extraction of their functional core as well as annotation of possible changes likely to appear in different signs’ segments. Accordingly, each utterance was subdivided into segments of motion and hold, and the resulting sign schemes were then entered into a separate directory containing information about the main parameters of each hold, including the possibilities of change due to immediate environment. The result of this work is a full decomposition of the signs, forming the database which can find its application in different statistical models of Russian Sign Language.Among other linguistic peculiarities of the database should be noted lexical variability of signs, mouthing and code switching between Russian sign language and “signed Russian”. Also, our signers learned Russian sign language in different regions of Russia, thus the collected data is potentially a source for research in dialectal variability of Russian sign language.

Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others

The predominant method of communication for hearing-impaired and deaf people is still sign language. It is a carefully constructed hand gesture language, and each motion denotes a certain meaning. The purpose of this paper is to create a system for Arabic Sign Language automatic translation. The proposed Arabic sign to Text System consists of five primary stages and serves as a translator for deaf and dumb persons and normal people to improve communication. This system depends on building a two datasets image features for Arabic sign language gestures alphabets from two resources: the Arabic Sign Language dictionary and gestures from different signers' humans. It also uses gesture recognition techniques, which lets the user interact with the outside world. Video and images capture, Video and images processing, Hand Signs construction, classification, and finally text transformation and interpretation. In this paper, we use a set of appropriate features in step hand sign construction and classification of based on different classification algorithms such as (KNN, MLP and C4.5) and compare these results to get better classifier. This system offers a novel technique of hand detection that detects and extracts hand gestures of Arabic Sign from Image or Video..

Developing a Rule-Based Machine-Translation System, Ewondo–French–Ewondo

Machine translation (MT) significantly contributes to democratizing access to textual information across multiple languages and is established as a dynamic language service in the global multilingual society. Not surprisingly, the attractiveness of the MT market has stirred up spectacular innovations, driven by artificial intelligence in the digital technology industry. The commercial stakes of the industry has led to massive investments in the development of automatic translation systems for languages of wider communication and an increased marginalization of minority languages in this avenue. This article reports on the on-going development of a low-tech, rule-based MT system for Ewondo, a Bantu low-resourced language spoken in Cameroon. The project aims to fill the gap in access to MT services in the target minority language community and to generate parallel corpora from and into the Ewondo language.

Suffixation Rules of Manipuri Verbs in English to Manipuri Machine Translation

The suffixation of verbs plays an important role in the formation of a meaningful sentence in Manipuri language. All the verbs in Manipuri are bound roots. So, there is always a suffix at the verb in Manipuri sentences. In English language, the tense of a sentence is determined from the verb but in Manipuri language, the tense of a sentence is determined from the suffix(es) associated with the verb. English to Manipuri Machine Translation (EMMT) is a rule-based automatic translation system. In EMMT, the suffixation rules of Manipuri verbs are essential for the derivation of an appropriate suffix(es) of the translated verb in Manipuri from the input English sentence. In this MT, suffixation rules are proposed to use in the post-processing module as a new approach. This paper presents the process of EMMT, different suffixation rules of Manipuri verbs and implementation in EMMT according to the types of tense in English.

Selecting a set of semantic labels to eliminate ambiguity for Vietnamese

The rapid development of automatic control systems in natural language, automatic translation systems based on semantic statistics have been receiving much attention from computer science researchers. However, this method requires a large bilingual corpus and accurate semantic tagging, the construction of which requires a lot of time and effort, because of the ambiguity of the natural language. For Vietnamese, automatic question-and-answer systems are increasingly developing in Vietnam, but the problem of semantic ambiguity has not yet received much attention from domestic studies. In this paper, we build a model to evaluate and select an effective and reasonable set of semantic labels from 3 commonly used sets of semantic labels: LLOCE (Longman Lexicon of Contemporary English), LDOCE (Longman Dictionary) of Contemporary English) and WordNet. And then, select the appropriate set of labels, apply it to automatic semantic labeling systems for Vietnamese, help eliminate semantic ambiguity, and support automatic translation, automatic question-and-answer systems efficiently.

A Corpus for Evaluation of Cross Language Text Re-use Detection Systems

In recent years, the availability of documents through the Internet along with automatic translation systems have increased plagiarism, especially across languages. Cross-lingual plagiarism occurs when the source or original text is in one language and the plagiarized or re-used text is in another language. Various methods for automatic text re-use detection across languages have been developed whose objective is to assist human experts in analyzing documents for plagiarism cases. For evaluating the performance of these systems and algorithms, standard evaluation resources are needed. To construct cross lingual plagiarism detection corpora, the majority of earlier studies have paid attention to English and other European language pairs, and have less focused on low resource languages. In this paper, we investigate a method for constructing an English-Persian cross-language plagiarism detection corpus based on parallel bilingual sentences that artificially generate passages with various degrees of paraphrasing. The plagiarized passages are inserted into topically related English and Persian Wikipedia articles in order to have more realistic text documents. The proposed approach can be applied to other less-resourced languages. In order to evaluate the compiled corpus, both intrinsic and extrinsic evaluation methods were employed. So, the compiled corpus can be suitably included into an evaluation framework for assessing cross-language plagiarism detection systems. Our proposed corpus is free and publicly available for research purposes.

A prototype Malayalam to Indian Sign Language (ISL) Automatic Translator

Sign Language is one of the major mediums of communication for deaf people. It uses gestures which makes use of hand, facial expression and other body parts to convey meaning. The use of parallel modification as opposed to the sequential nature of spoken language makes it difficult for efficient representation. This calls for the development of animation techniques for representing signs which can easily model human gestures. This paper presents a prototype Malayalam language text to Indian Sign Language (ISL) automatic translation system. The proposed system takes Malayalam text as input and generates corresponding Indian Sign Language (ISL) signs as output. Output animation is rendered using a computer-generated model. This system will help to disseminate information to deaf people in public utility places like railways, banks, hospitals etc. This will also act as an educational tool.

Blockchain Smart Contract to Prevent Forgery of Degree Certificates: Artificial Intelligence Consensus Algorithm

Certificates are often falsified, such as fake diplomas and forged transcripts. As such, many schools and educational institutions have begun to issue diplomas online. Although diplomas can be issued conveniently anytime, anywhere, there are many cases wherein diplomas are forged through hacking and forgery. This paper deals with the required Blockchain diploma. In addition, we use an automatic translation system, which incorporates natural language processing, to perform verification work that does not require an existing public certificate. The hash algorithm is used to authenticate security. This paper also proposes the use of these security protocols to provide more secure data protection. In addition, each transaction history, whether a diploma is true or not, may be different in length if it is presented in text, but converting it into a hash function means that it is always more than a certain length of SHA-512 or higher. It is then verified using the time stamp values. These chaining codes are designed. This paper also provides the necessary experimental environment. At least 10 nodes are constructed. Blockchain platform development applies and references Blockchain standardization, and a platform test, measurement test, and performance measurement test are conducted to assess the smart contract development and performance measurement. A total of 500 nodes were obtained by averaging 200 times, and a Blockchain-based diploma file was agreed upon at the same time. It shows performance information of about 4100 TPS. In addition, the analysis of artificial intelligence distribution diagram was conducted using a four-point method, and the distribution chart was evenly distributed, confirming the diploma with the highest similarity. The verified values were then analyzed. This paper proposes these natural language processing-based Blockchain algorithms.