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Journal articles on the topic 'Language identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Suganthi, Mrs Dr V., C. Thavapriya, and T. Mirudhu Bashini. "Sign Language Identification." International Journal of Research Publication and Reviews 5, no. 3 (2024): 5997–6001. http://dx.doi.org/10.55248/gengpi.5.0324.0855.

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Kumar, P. Vijay, and A. Raviteja A. Raviteja. "Automatic Indian Language Identification." International Journal of Scientific Research 2, no. 4 (2012): 79–82. http://dx.doi.org/10.15373/22778179/apr2013/31.

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MALMASI, SHERVIN, and MARK DRAS. "Multilingual native language identification." Natural Language Engineering 23, no. 2 (2015): 163–215. http://dx.doi.org/10.1017/s1351324915000406.

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AbstractWe present the first comprehensive study of Native Language Identification (NLI) applied to text written in languages other than English, using data from six languages. NLI is the task of predicting an author’s first language using only their writings in a second language, with applications in Second Language Acquisition and forensic linguistics. Most research to date has focused on English but there is a need to apply NLI to other languages, not only to gauge its applicability but also to aid in teaching research for other emerging languages. With this goal, we identify six typologica

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Alam, Salman. "Comparison of Various Models in the Context of Language Identification (Indo Aryan Languages)." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 185–88. https://doi.org/10.21275/sr21303115028.

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PRATHIBA, GEETHA, VELPULA SHRAVYA PATEL, PATHAK SHIVANI, and UPPUTALLA DIVYA. "LANGUAGE IDENTIFICATION FOR MULTILINGUAL MACHINE." Journal of Engineering Sciences 15, no. 10 (2024): 202–12. http://dx.doi.org/10.36893/jes.2024.v15i10.025.

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Language Identification for Multilingual Machine Translation is a crucial component in modern natural language processing systems, enabling accurate and efficient translation across multiple languages. This paper presents a comprehensive approach to language identification that enhances the performance of multilingual machine translation systems.The proposed method utilizes advanced machine learning techniques to automatically detect the language of a given text with high accuracy. By incorporating a variety of linguistic features and leveraging large-scale multilingual datasets, the system ca

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Koena, Mabokela. "INTEGRATION OF PHONOTACTIC FEATURES FOR LANGUAGE IDENTIFICATION ON CODE-SWITCHED SPEECH." International Journal on Natural Language Computing (IJNLC) 11, no. 1 (2022): 14. https://doi.org/10.5281/zenodo.6375524.

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In this paper, phoneme sequences are used as language information to perform code-switched language identification (LID). With the one-pass recognition system, the spoken sounds are converted into phonetically arranged sequences of sounds. The acoustic models are robust enough to handle multiple languages when emulating multiple hidden Markov models (HMMs). To determine the phoneme similarity among our target languages, we reported two methods of phoneme mapping. Statistical phoneme-based bigram language models (LM) are integrated into speech decoding to eliminate possible phone mismatches. Th

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Qafmolla, Nejla. "Automatic Language Identification." European Journal of Language and Literature 7, no. 1 (2017): 140. http://dx.doi.org/10.26417/ejls.v7i1.p140-150.

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Automatic Language Identification (LID) is the process of automatically identifying the language of spoken utterance or written material. LID has received much attention due to its application to major areas of research and long-aspired dreams in computational sciences, namely Machine Translation (MT), Speech Recognition (SR) and Data Mining (DM). A considerable increase in the amount of and access to data provided not only by experts but also by users all over the Internet has resulted into both the development of different approaches in the area of LID – so as to generate more efficient syst

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Zissman, Marc A., and Kay M. Berkling. "Automatic language identification." Speech Communication 35, no. 1-2 (2001): 115–24. http://dx.doi.org/10.1016/s0167-6393(00)00099-6.

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Van Segbroeck, Maarten, Ruchir Travadi, and Shrikanth S. Narayanan. "Rapid Language Identification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 7 (2015): 1118–29. http://dx.doi.org/10.1109/taslp.2015.2419978.

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Ranasinghe, Tharindu, and Marcos Zampieri. "Multilingual Offensive Language Identification for Low-resource Languages." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (2022): 1–13. http://dx.doi.org/10.1145/3457610.

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Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this article, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We

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Botha, G., V. Zimu, and E. Barnard. "Text-based language identification for south african languages." SAIEE Africa Research Journal 98, no. 4 (2007): 141–46. http://dx.doi.org/10.23919/saiee.2007.9485636.

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Jothilakshmi, S., V. Ramalingam, and S. Palanivel. "A hierarchical language identification system for Indian languages." Digital Signal Processing 22, no. 3 (2012): 544–53. http://dx.doi.org/10.1016/j.dsp.2011.11.008.

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13

Gamallo, Pablo, José Ramom Pichel, and Iñaki Alegria. "From language identification to language distance." Physica A: Statistical Mechanics and its Applications 484 (October 2017): 152–62. http://dx.doi.org/10.1016/j.physa.2017.05.011.

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Baimyrza, A. "LANGUAGE IDENTIFICATION PROCESSES OF THE YOUTH." Tiltanym, no. 3 (September 30, 2021): 28–36. http://dx.doi.org/10.55491/2411-6076-2021-3-28-36.

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The article deals with the role of the Russian language in the processes of language identification of students. The results of the sociolinguistic survey, the main objectives of which were determined based on the need to obtain information on the following aspects of the language situation: the degree of knowledge of the youth in the state, Russian and other languages; the level and nature of social preferences in relation to the use of languages in various spheres of life; the nature of social and language preferences of the young population. The review of theoretical works of Kazakhstan and

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Qasim, Mamtimin, Wushour Silamu, and Minghui Qiu. "The Design of a Script Identification Algorithm and Its Application in Constructing a Text Language Identification Dataset." Data 9, no. 11 (2024): 134. http://dx.doi.org/10.3390/data9110134.

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Script identification is easier to implement than language identification, and its identification rate is very high. The fewer languages are identified when using a language identification algorithm, the higher the identification rate is. However, no systematic study on SI involving multiple languages and determining how to construct relevant language identification datasets has been conducted. Therefore, in this paper, we discuss and design a script identification algorithm and the construction of a language identification dataset based on script groups. The data sources in this paper compris

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Deshpande, Mr Onkar. "Postal Address Identification and Sorting." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4946–53. http://dx.doi.org/10.22214/ijraset.2021.36023.

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In this fast-moving world, a normal man can take considerable time to find a postal card in a bunch of postcards with significant issues like unclear handwriting, having trouble recognizing some uncommon or ambiguous names. Also, in postal offices or industries, it negatively impacts the efficiency of the postal system. I am making a system for Indian postal automation based on recognizing pin-code on the postcard. In India, there are multiple languages were speak. Indian postcards are mainly written in three languages the state's official language, English, and Devanagari language. In India,

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Shimi, G., C. Jerin Mahibha, and Durairaj Thenmozhi. "An Empirical Analysis of Language Detection in Dravidian Languages." Indian Journal Of Science And Technology 17, no. 15 (2024): 1515–26. http://dx.doi.org/10.17485/ijst/v17i15.765.

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Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches wi

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Del Bonifro, Francesca, Maurizio Gabbrielli, Antonio Lategano, and Stefano Zacchiroli. "Image-based many-language programming language identification." PeerJ Computer Science 7 (July 23, 2021): e631. http://dx.doi.org/10.7717/peerj-cs.631.

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Programming language identification (PLI) is a common need in automatic program comprehension as well as a prerequisite for deeper forms of code understanding. Image-based approaches to PLI have recently emerged and are appealing due to their applicability to code screenshots and programming video tutorials. However, they remain limited to the recognition of a small amount of programming languages (up to 10 languages in the literature). We show that it is possible to perform image-based PLI on a large number of programming languages (up to 149 in our experiments) with high (92%) precision and

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19

Barnard, Etienne, and Yonghong Yan. "Toward new language adaptation for language identification." Speech Communication 21, no. 4 (1997): 245–54. http://dx.doi.org/10.1016/s0167-6393(97)00009-5.

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Sujaini, Herry, and Arif Bijaksana Putra. "Analysis of language identification algorithms for regional Indonesian languages." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1741. http://dx.doi.org/10.11591/ijai.v13.i2.pp1741-1752.

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Detecting local languages in Indonesia is essential for recognizing linguistic diversity, promoting intercultural understanding, preserving endangered languages, and improving access to education and services. By identifying and documenting these languages, we can support language preservation efforts, provide tailored resources for communities, and celebrate the unique cultural heritage of different ethnic groups. Ultimately, this encourages a more accepting and open-minded society, prioritizing various languages and cultural customs. This research aims to identify the most suitable algorithm

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Herry, Sujaini, and Bijaksana Putra Arif. "Analysis of language identification algorithms for regional Indonesian languages." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1741–52. https://doi.org/10.11591/ijai.v13.i2.pp1741-1752.

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Detecting local languages in Indonesia is essential for recognizing linguistic diversity, promoting intercultural understanding, preserving endangered languages, and improving access to education and services. By identifying and documenting these languages, we can support language preservation efforts, provide tailored resources for communities, and celebrate the unique cultural heritage of different ethnic groups. Ultimately, this encourages a more accepting and open-minded society, prioritizing various languages and cultural customs. This research aims to identify the most suitable algorithm

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22

Orena, Adriel John, Linda Polka, and Rachel M. Theodore. "Language familiarity mediates identification of bilingual talkers across languages." Journal of the Acoustical Society of America 140, no. 4 (2016): 3227. http://dx.doi.org/10.1121/1.4970197.

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23

Singh, Gundeep, Sahil Sharma, Vijay Kumar, Manjit Kaur, Mohammed Baz, and Mehedi Masud. "Spoken Language Identification Using Deep Learning." Computational Intelligence and Neuroscience 2021 (September 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/5123671.

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The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian

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24

Muthusamy, Y. K., E. Barnard, and R. A. Cole. "Reviewing automatic language identification." IEEE Signal Processing Magazine 11, no. 4 (1994): 33–41. http://dx.doi.org/10.1109/79.317925.

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Ambikairajah, Eliathamby, Haizhou Li, Liang Wang, Bo Yin, and Vidhyasaharan Sethu. "Language Identification: A Tutorial." IEEE Circuits and Systems Magazine 11, no. 2 (2011): 82–108. http://dx.doi.org/10.1109/mcas.2011.941081.

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26

van Bezooijen, Renée, and Charlotte Gooskens. "Identification of Language Varieties." Journal of Language and Social Psychology 18, no. 1 (1999): 31–48. http://dx.doi.org/10.1177/0261927x99018001003.

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Thomas, Merin, Dr Latha C A, and Antony Puthussery. "Identification of language in a cross linguistic environment." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (2020): 544. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp544-548.

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<p class="normal">World has become very small due to software internationationalism. Applications of machine translations are increasing day by day. Using multiple languages in the social media text is an developing trend. .Availability of fonts in the native language enhanced the usage of native text in internet communications. Usage of transliterations of language has become quite common. In Indian scenario current generations are familiar to talk in native language but not to read and write in the native language, hence they started using English representation of native language in t

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Merin, Thomas, C. A. Latha, and Puthussery Antony. "Identification of language in a cross linguistic environment." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 1 (2020): 544–48. https://doi.org/10.11591/ijeecs.v18.i1.pp544-548.

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World has become very small due to software internationationalism. Applications of machine translations are increasing day by day. Using multiple languages in the social media text is a developing trend. Availability of fonts in the native language enhanced the usage of native text in internet communications. Usage of transliterations of language has become quite common. In Indian scenario current generations are familiar to talk in native language but not to read and write in the native language, hence they started using English representation of native language in textual messages. This pape

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Nugraha, Azhar Baihaqi, and Ade Romadhony. "Identification of 10 Regional Indonesian Languages Using Machine Learning." sinkron 8, no. 4 (2023): 2203–14. http://dx.doi.org/10.33395/sinkron.v8i4.12989.

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Language Identification plays a pivotal role in deciphering the rich tapestry of Indonesia's diverse regional languages, encompassing a wide spectrum of scripts, and spoken forms. Language Identification, an integral component of Natural Language Processing, is frequently addressed through Text Classification. In this study, we embark on the task of identifying 10 Indonesian languages, leveraging the NusaX dataset, with the overarching objective of contextual language determination. To achieve this, we harness a diverse array of machine learning techniques, including Support Vector Machine, Na

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N.S, Shvaikina, and Laryushkina E.E. "Identification of Ways to Prevent and Overcome the Language Barrier in Foreign Language Classes." Addiction Research and Adolescent Behaviour 5, no. 3 (2022): 01–02. http://dx.doi.org/10.31579/2688-7517/046.

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Some students lose their motivation to learn a foreign language at school, as they may have had a negative experience. In order for a teacher to increase motivation to learn a second language, it is necessary to create a situation of success.

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Kumar, Gaurav, and Saurabh Bhardwaj. "Biomimetic Computing for Efficient Spoken Language Identification." Biomimetics 10, no. 5 (2025): 316. https://doi.org/10.3390/biomimetics10050316.

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Spoken Language Identification (SLID)-based applications have become increasingly important in everyday life, driven by advancements in artificial intelligence and machine learning. Multilingual countries utilize the SLID method to facilitate speech detection. This is accomplished by determining the language of the spoken parts using language recognizers. On the other hand, when working with multilingual datasets, the presence of multiple languages that have a shared origin presents a significant challenge for accurately classifying languages using automatic techniques. Further, one more chall

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Hidayatullah, Ahmad Fathan, Rosyzie Anna Apong, Daphne T. C. Lai, and Atika Qazi. "Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets." PeerJ Computer Science 9 (June 22, 2023): e1312. http://dx.doi.org/10.7717/peerj-cs.1312.

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With the massive use of social media today, mixing between languages in social media text is prevalent. In linguistics, the phenomenon of mixing languages is known as code-mixing. The prevalence of code-mixing exposes various concerns and challenges in natural language processing (NLP), including language identification (LID) tasks. This study presents a word-level language identification model for code-mixed Indonesian, Javanese, and English tweets. First, we introduce a code-mixed corpus for Indonesian-Javanese-English language identification (IJELID). To ensure reliable dataset annotation,

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Carpenter,, Pramod. "Language Identifier." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36148.

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This project presents a language identifier system focused on achieving high accuracy in identifying a specific set of 22 languages: Estonian, Swedish, English, Russian, Romanian, Persian, Pashto, Spanish, Hindi, Korean, Chinese, French, Portuguese, Indonesian, Urdu, Latin, Turkish, Japanese, Dutch, Tamil, Thai, and Arabic. Existing LI systems might struggle with the nuances of these languages, often prioritizing identification of more common languages. Our targeted approach allows for tailored optimization to achieve superior accuracy. We employ the Multinomial Naive Bayes (MNB) algorithm due

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Irtza, Saad, Vidhyasaharan Sethu, Eliathamby Ambikairajah, and Haizhou Li. "Using language cluster models in hierarchical language identification." Speech Communication 100 (June 2018): 30–40. http://dx.doi.org/10.1016/j.specom.2018.04.004.

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Avram, Andrei-Marius, Verginica Barbu Mititelu, Vasile Păiș, Dumitru-Clementin Cercel, and Ștefan Trăușan-Matu. "Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation." Mathematics 11, no. 11 (2023): 2548. http://dx.doi.org/10.3390/math11112548.

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Correctly identifying multiword expressions (MWEs) is an important task for most natural language processing systems since their misidentification can result in ambiguity and misunderstanding of the underlying text. In this work, we evaluate the performance of the mBERT model for MWE identification in a multilingual context by training it on all 14 languages available in version 1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language adversarial training into our methodology to create language-independent embeddings and improve its capabilities in identifying multiword e

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Selamat, Ali, and Nicholas Akosu. "Word-length algorithm for language identification of under-resourced languages." Journal of King Saud University - Computer and Information Sciences 28, no. 4 (2016): 457–69. http://dx.doi.org/10.1016/j.jksuci.2014.12.004.

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Chakravarthi, Bharathi Raja, Manoj Balaji Jagadeeshan, Vasanth Palanikumar, and Ruba Priyadharshini. "Offensive language identification in dravidian languages using MPNet and CNN." International Journal of Information Management Data Insights 3, no. 1 (2023): 100151. http://dx.doi.org/10.1016/j.jjimei.2022.100151.

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Asubiaro, Toluwase, Tunde Adegbola, Robert Mercer, and Isola Ajiferuke. "A word‐level language identification strategy for resource‐scarce languages." Proceedings of the Association for Information Science and Technology 55, no. 1 (2018): 19–28. http://dx.doi.org/10.1002/pra2.2018.14505501004.

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Menon, Riya. "Detectsy: A System for Detecting Language from the Text, Images, and Audio Files." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1975–80. http://dx.doi.org/10.22214/ijraset.2022.44281.

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Abstract— Language detection is a natural language processing task where we need to identify the language of a text or document. As a human, we can easily detect the languages we know. However, it is not possible for an individual to identify many languages. This is where the language identification task can be used. The proposed solution is a complete system that detects language from the text, images, and audio files. Language identification task from text is carried out by training a Multinomial Naive Bayes classifier model. In the case of image and audio inputs, Python libraries are used t

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40

Jauhiainen, T., K. Lindén, and H. Jauhiainen. "Language model adaptation for language and dialect identification of text." Natural Language Engineering 25, no. 5 (2019): 561–83. http://dx.doi.org/10.1017/s135132491900038x.

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AbstractThis article describes an unsupervised language model (LM) adaptation approach that can be used to enhance the performance of language identification methods. The approach is applied to a current version of the HeLI language identification method, which is now called HeLI 2.0. We describe the HeLI 2.0 method in detail. The resulting system is evaluated using the datasets from the German dialect identification and Indo-Aryan language identification shared tasks of the VarDial workshops 2017 and 2018. The new approach with LM adaptation provides considerably higher F1-scores than the bas

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Ranasinghe, Tharindu, and Marcos Zampieri. "An Evaluation of Multilingual Offensive Language Identification Methods for the Languages of India." Information 12, no. 8 (2021): 306. http://dx.doi.org/10.3390/info12080306.

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The pervasiveness of offensive content in social media has become an important reason for concern for online platforms. With the aim of improving online safety, a large number of studies applying computational models to identify such content have been published in the last few years, with promising results. The majority of these studies, however, deal with high-resource languages such as English due to the availability of datasets in these languages. Recent work has addressed offensive language identification from a low-resource perspective, exploring data augmentation strategies and trying to

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Lui, Marco, Jey Han Lau, and Timothy Baldwin. "Automatic Detection and Language Identification of Multilingual Documents." Transactions of the Association for Computational Linguistics 2 (December 2014): 27–40. http://dx.doi.org/10.1162/tacl_a_00163.

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Language identification is the task of automatically detecting the language(s) present in a document based on the content of the document. In this work, we address the problem of detecting documents that contain text from more than one language ( multilingual documents). We introduce a method that is able to detect that a document is multilingual, identify the languages present, and estimate their relative proportions. We demonstrate the effectiveness of our method over synthetic data, as well as real-world multilingual documents collected from the web.

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Barlas, P., D. Hebert, C. Chatelain, S. Adam, and T. Paquet. "Language Identification in Document Images." Electronic Imaging 2016, no. 17 (2016): 1–16. http://dx.doi.org/10.2352/issn.2470-1173.2016.17.drr-058.

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Sadhukhan, Tanusree, Shweta Bansal, and Atul Kumar. "Automatic Identification of Spoken Language." IOSR Journal of Computer Engineering 19, no. 02 (2017): 84–89. http://dx.doi.org/10.9790/0661-1902058489.

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Barlas, P., D. Hebert, C. Chatelain, S. Adam, and T. Paquet. "Language Identification in Document Images." Journal of Imaging Science and Technology 60, no. 1 (2016): 104071–1040716. http://dx.doi.org/10.2352/j.imagingsci.technol.2016.60.1.010407.

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., Shubham Saini. "LANGUAGE IDENTIFICATION USING G-LDA." International Journal of Research in Engineering and Technology 02, no. 11 (2013): 42–45. http://dx.doi.org/10.15623/ijret.2013.0211008.

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Mahender, C. Namrata, Ramesh Ram Naik, and Maheshkumar Bhujangrao Landge. "Author Identification for Marathi Language." Advances in Science, Technology and Engineering Systems Journal 5, no. 2 (2020): 432–40. http://dx.doi.org/10.25046/aj050256.

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Hazen, Timothy J., and Victor W. Zue. "Segment-based automatic language identification." Journal of the Acoustical Society of America 101, no. 4 (1997): 2323–31. http://dx.doi.org/10.1121/1.418211.

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Li, Kung-Pu. "Automatic language identification/verification system." Journal of the Acoustical Society of America 104, no. 1 (1998): 31. http://dx.doi.org/10.1121/1.424049.

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Dutta, Arup Kumar, and K. Sreenivasa Rao. "Language identification using phase information." International Journal of Speech Technology 21, no. 3 (2017): 509–19. http://dx.doi.org/10.1007/s10772-017-9482-5.

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