Journal articles: 'Assamese language'

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Ahmed, Iftikar Ali, Baishalee Rajkhowa, and Arup Kumar Nath. "Linguistic Imperialism: A Study of its Impact on the Assamese Language in the Greater Sivasagar District of Assam." Indian Journal of Language and Linguistics 4, no. 2 (June 16, 2023): 6–17. http://dx.doi.org/10.54392/ijll2322.

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The state of Assam in India is the home to the people who speak Assamese, an Indo-Aryan language. Assamese is the native tongue of the people of Assam and the official language of the state of Assam. Based on linguistic standards and conventions, Assamese is a vital language for writing. However, when we attempt to see the language from the viewpoint of native speakers' attitudes towards the language, we find that the language is steadily deteriorating among the linguistic community. This deterioration is caused by Linguistic Imperialism. Linguistic Imperialism is a phenomenon in which a dominant language attempts to weaken other languages both socially and politically and in a theoretically founded way. The impact of the dominance is increasing day by day due to which a negative attitude has increased significantly among the native speakers of Assamese who considers English as superior to their mother tongue. Negative attitude is one of the reasons of language endangerment and we cannot deny the possibility of endangerment of the Assamese language in the far future if the dominance of English goes on increasing. History is evident that languages with a huge literature and population got extinct because of the reasons like negative attitude, dominance of other languages, decreasing rate of fluent native speakers, examples of such languages are Sanskrit, Hebrew, etc. This paper tries to analyse the negative attitude which is gradually increasing in the Assamese language and ways to strengthen it by reverting the dominance of Linguistic Imperialism by languages like English and Hindi.

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Gogoi, Arjun, and Nomi Baruah. "A Lemmatizer for Low-resource Languages: WSD and Its Role in the Assamese Language." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (July 31, 2022): 1–22. http://dx.doi.org/10.1145/3502157.

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The morphological variations of highly inflected languages that appear in a text impede the progress of computer processing and root word determination tasks while extracting an abstract. As a remedy to this difficulty, a lemmatization algorithm is developed, and its effectiveness is evaluated for Word Sense Disambiguation (WSD). Having observed its usefulness, lemmatizer is considered for developing Natural Language Processing tools for languages rich in morphological variations. Among various Indian highly inflected languages, Assamese, spoken by over 14 million people in the North-Eastern region of India, is also one of them. In this present work, after a detailed study on the possible transformations through which surface words are created from lemmas, we have designed an Assamese lemmatizer in such a manner that suitable reverse transformations can be employed on a surface word to derive the co-relative (similar) lemma back. And it has been observed that the lemmatizer is competent to deal with inflectional and derivational morphology in Assamese, and the same was evaluated on various Assamese articles extracted from the Assamese Corpus consisting of 50,000 surface words (excluding proper nouns), and the result that it yielded with 82% accuracy was quite encouraging and satisfying, as Assamese is a low-level language and no research work has been done in the Assamese language regarding the lemmatization of words. Considering the result obtained, the lemmatizer is then evaluated for Assamese WSD. For this purpose, 10 highly polysemous Assamese words are taken into account for sense disambiguation. We have also regarded varied WSD systems and observed that such systems enhance the effectiveness of all the WSD systems, which is statistically significant.

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Mahanta, Shakuntala. "Assamese." Journal of the International Phonetic Association 42, no. 2 (August 2012): 217–24. http://dx.doi.org/10.1017/s0025100312000096.

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The variety described here is representative of colloquial Assamese spoken in the eastern districts of Assam. Assam is a North-Eastern state of India, therefore Assamese and creoles of Assamese like Nagamese are spoken in the different North-Eastern states of Nagaland, Arunachal Pradesh, Meghalaya, and also the neighbouring country of Bhutan. Approximately 15 million people speak Assamese in India (seeEthnologue, Gordon 2005, which lists 15,374,000 speakers including those in Bhutan and Bangladesh). In the pre-British era (until 1826), the kingdom of Assam was ruled by Ahom kings and the then capital was based in the Eastern district of Sibsagar and later in Jorhat. American missionaries established the first printing press in Sibsagar and in the year 1846 published a monthly periodicalArunodoiusing the variety spoken in and around Sibsagar as the point of departure. This is the immediate reason which led to the acceptance of the formal variety spoken in eastern Assam (which roughly comprises of all the districts of Upper Assam). Having said that, the language spoken in these regions of Assam also show a certain degree of variation from the written form of the ‘standard’ language. As against the relative homogeneity of the variety spoken in eastern Assam, variation is considerable in certain other districts which would constitute the western part of Assam, comprising of the district of Kamrup up to Goalpara and Dhubri (see also Kakati 1962 and Grierson 1968). In contemporary Assam, for the purposes of mass media and communication, a certain neutral blend of eastern Assamese, without too many distinctive eastern features, like /ɹ/ deletion, which is a robust phenomenon in the eastern varieties, is still considered to be the norm. The lexis of Assamese is mainly Indo-Aryan, but it also has a sizeable amount of lexical items related to Bodo among other Tibeto-Burman languages (Kakati 1962), and there are a substantial number of items borrowed from Hindi, English and Bengali in recent times.

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Daimary, Surjya Kanta, Vishal Goyal, Madhumita Barbora, and Umrinderpal Singh. "Development of Part of Speech Tagger for Assamese Using HMM." International Journal of Synthetic Emotions 9, no. 1 (January 2018): 23–32. http://dx.doi.org/10.4018/ijse.2018010102.

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This article presents the work on the Part-of-Speech Tagger for Assamese based on Hidden Markov Model (HMM). Over the years, a lot of language processing tasks have been done for Western and South-Asian languages. However, very little work is done for Assamese language. So, with this point of view, the POS Tagger for Assamese using Stochastic Approach is being developed. Assamese is a free word-order, highly agglutinate and morphological rich language, thus developing POS Tagger with good accuracy will help in development of other NLP task for Assamese. For this work, an annotated corpus of 271,890 words with a BIS tagset consisting of 38 tag labels is used. The model is trained on 256,690 words and the remaining words are used in testing. The system obtained an accuracy of 89.21% and it is being compared with other existing stochastic models.

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Boruah, Radhika. "Pro-drop and subject pronouns in Assamese." International Journal of Linguistics, Literature and Translation 3, no. 7 (July 31, 2020): 210–14. http://dx.doi.org/10.32996/ijllt.2020.3.7.23.

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In recent years a considerable attention has been given to the study of control and pro-drop. Languages with rich agreement patterns allow for phonetically empty subject which is called “pro”. This paper deals with the pro-drop phenomena of Assamese. The main objective of the paper is to give a descriptive analysis of the subject pronouns and their nature in the pro-drop phenomena. The paper also aims to give a basic idea of this pro-drop phenomenon and shows how certain subject pronouns behave differently in Assamese. Pro drop in Assamese is a major linguistic characteristic of the language. The findings of the study revealed that we can drop most of the subject pronouns in Assamese. Though Assamese is considered as a pro-drop language, this phenomenon is not acceptable in written language. The sentences should be in a full structural representation in written language. In other words, we can say that pro-drop is used in our daily conversations; it is more or less like informal conversations.

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Laskar, Sahinur Rahman, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, and Sivaji Bandyopadhyay. "Improved neural machine translation for low-resource English–Assamese pair." Journal of Intelligent & Fuzzy Systems 42, no. 5 (March 31, 2022): 4727–38. http://dx.doi.org/10.3233/jifs-219260.

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Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

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Baruah, Rupjyoti, Rajesh Kumar Mundotiya, and Anil Kumar Singh. "Low Resource Neural Machine Translation: Assamese to/from Other Indo-Aryan (Indic) Languages." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–32. http://dx.doi.org/10.1145/3469721.

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Machine translation (MT) systems have been built using numerous different techniques for bridging the language barriers. These techniques are broadly categorized into approaches like Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). End-to-end NMT systems significantly outperform SMT in translation quality on many language pairs, especially those with the adequate parallel corpus. We report comparative experiments on baseline MT systems for Assamese to other Indo-Aryan languages (in both translation directions) using the traditional Phrase-Based SMT as well as some more successful NMT architectures, namely basic sequence-to-sequence model with attention, Transformer, and finetuned Transformer. The results are evaluated using the most prominent and popular standard automatic metric BLEU (BiLingual Evaluation Understudy), as well as other well-known metrics for exploring the performance of different baseline MT systems, since this is the first such work involving Assamese. The evaluation scores are compared for SMT and NMT models for the effectiveness of bi-directional language pairs involving Assamese and other Indo-Aryan languages (Bangla, Gujarati, Hindi, Marathi, Odia, Sinhalese, and Urdu). The highest BLEU scores obtained are for Assamese to Sinhalese for SMT (35.63) and the Assamese to Bangla for NMT systems (seq2seq is 50.92, Transformer is 50.01, and finetuned Transformer is 50.19). We also try to relate the results with the language characteristics, distances, family trees, domains, data sizes, and sentence lengths. We find that the effect of the domain is the most important factor affecting the results for the given data domains and sizes. We compare our results with the only existing MT system for Assamese (Bing Translator) and also with pairs involving Hindi.

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Shikhar Kumar Sarma, Kuwali Talukdar,. "Enabling Natural Language Processing and AI Research in Low-Resource Languages: Development and Description of an Assamese UPoS Tagged Dataset." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 1312–20. http://dx.doi.org/10.52783/jes.1506.

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This paper describes in detail the Universal Parts of Speech (UPoS) tagged dataset for the Assamese language. PoS tagged dataset in a language is crucial for experimenting and creating resources for various Natural Language Processing (NLP) and AI research. With the growing usage of Universal Dependency standards, tagged dataset with Universal PoS tags are becoming very much essential for contemporary experiments in the NLP community. NLP research in Assamese, and Indo-Aryan language, is relatively new, and the language is considered a Low Resource language. The dataset of UPoS tagged Assamese text is created with an aim of contributing towards enriching this low resource language for NLP and AI tasks. The size of the dataset is 283506 tokens of Assamese vocabulary, against total 20280 sentences, tagged with 17 standard UPoS tags of core lexical categories. The raw data are taken from an open-source corpus originally tagged with BIS tagset. The original size of 453457 tokens against 29504 sentences, after subjected to data filtering, was reduced to this clean resource of 283506 tokens. Lexical categories mapping is done with linguistic expertise, from BIS to UPoS tagsets. Mapped pattern was used for a first-level conversion of BIS tags to UPoS tags. Linguistic validation is also performed with linguistic experts and inter annotator agreement/disagreements were recorded. Second level validation resulted in deciding on the agreement, producing the final version of the dataset. This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.

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Bharadwaj, Lopamudra, Urmi Roy, Jyoti Ram, Haripriya Telakkadan, and B. P. Abhishek. "Speech Emotion Recognition in Native and Nonnative Languages." Journal of Indian Speech Language & Hearing Association 38, no. 1 (January 2024): 24–28. http://dx.doi.org/10.4103/jisha.jisha_11_23.

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Abstract Background: Speech emotion recognition can be experimentally tested in native and nonnative languages, as the mechanisms involved would differ across these languages. The recognition of speech in the native language is mediated through the language, and in nonnative languages, speech emotion recognition is facilitated through affective prosody. Linguistic prosody is anchored by the left hemisphere, whereas the right hemisphere coordinates the right hemisphere. The current study was carried out with the aim of testing speech emotion recognition in native and nonnative speakers with the motive of assessing the basic role of language in mediating prosody. Methods: The study was carried out on native and nonnative speakers of Malayalam, Assamese, and Bengali. Fifteen sentences (5 declarative, 5 interrogative, and 5 exclamatory) were recorded from native speakers of the aforementioned languages; these sentences were played to native speakers and nonnative speakers of a given language. For instance, the sentences in Malayalam were played to native speakers of Malayalam and nonnative speakers (speakers of Assamese and Bengali). The response sheet had three smileys, and the participants were asked to mark the appropriate smiley after listening to the sentences. Each correct response was given a score of 1, whereas an incorrect response was given a score of 0. Results: The native speakers of Malayalam, Assamese, and Bengali secured scores of 13.14 and 15 in their native languages, respectively. In the nonnative language, the participants secured scores in the range of 2–5. The Kruskal–Wallis test showed a significant difference between the native and nonnative languages. The results depicted that the native speakers of a language were able to identify the speech emotions easily, whereas they had difficulty identifying the sentences in the other two languages. The same trend was observed for speakers of Malayalam, Assamese, and Bengali. This showed that speech emotion recognition was linguistically driven. Conclusions: The results highlight that the prosody is more linguistically driven as the performance in the native language was better than in nonnative languages.

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Bez, Gitanjali. "The relator noun construction in Assamese." Journal of South Asian Languages and Linguistics 7, no. 1 (March 1, 2020): 1–41. http://dx.doi.org/10.1515/jsall-2020-2023.

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Abstract This paper presents a comprehensive analysis of relator noun constructions in Assamese, an Indo-Aryan language spoken in the eastern part of India by a majority of people living in the state of Assam. The construction consists of a relator noun that functions as a head, and a genitive case marked noun that functions as a dependent. Semantically, most of the relator nouns encode spatial relation, such as place, path. However, some other relator nouns signal other relations, such as the ‘for’, ‘about’ etc. The occurrence of relator nouns is not an unusual phenomenon in Indo-Aryan languages. It has been analyzed as adpositions in many Indo-Aryan languages. However, I argue that the syntax of Assamese does not allow this analysis. It forms a distinct syntactic category, the behaviour of which is not similar to adpositions. Further, Assamese shares some close affinity regarding the relator noun construction with the neighbouring Tibeto-Burman languages such as Boro and Dimasa, rather than with the Indo-Aryan languages. Thus, this paper further investigates whether the resemblance occurs as a result of language contact or by accident.

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BARUAH, MANJEET. "Assamese Language, Narrative and the Making of the North East Frontier of India: Beyond Regional Indian Literary Studies." Modern Asian Studies 47, no. 2 (November 6, 2012): 652–81. http://dx.doi.org/10.1017/s0026749x12000716.

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AbstractThis paper is divided into two broad sections. The first section deals with the Brahmaputra Valley in Assam (north east India) and its transformation into a frontier in the nineteenth century. The section also deals with how this process was closely linked to the re-interpretation of the region's relationship with Indo-Gangetic culture, and the impact on development of the modern ‘Assamese’ language. The second section interprets modern Assamese novels in the light of the issues raised in the first section. It explores how issues such as indigeneity, the concept of India and modern Assamese language, share a relation of conflict in modern Assamese fiction. It is suggested in the conclusion that, due to such historical specificities, the language and narrative of the frontier require a specific regional approach, and should not be subsumed within larger frameworks such as ‘the nation’ or ‘South Asia’.

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Sharma, Bitopi, and P. H. Talukdar. "Phoneme based Dialect variation of Assamese Language." International Journal of Computer Sciences and Engineering 6, no. 12 (December 31, 2018): 238–40. http://dx.doi.org/10.26438/ijcse/v6i12.238240.

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Baruah, Nomi, Shikhar Kr Sarma, and Surajit Borkotokey. "Evaluation of Content Compaction in Assamese Language." Procedia Computer Science 171 (2020): 2275–84. http://dx.doi.org/10.1016/j.procs.2020.04.246.

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Satyanath, Shobha. "Contact, Diffusion and Divergence." Journal of Language Contact 16, no. 1 (March 20, 2024): 104–39. http://dx.doi.org/10.1163/19552629-01601001.

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Abstract The study discusses the outcomes of the contact and diffusion in two contact varieties of Assamese with respect to classifiers. The findings suggest that while classifiers have remained remarkably stable in their characteristics in Assamese over the past 160 years, during the same period, one of the contact varieties (Nagamese) has significantly diverged from the source language, and the other variety (Nefamese) displays greater continuity. The divergence in Nagamese is attributed to the substratal effect which has altered its characteristics associated with numerals and quantifiers in the source language, thereby making it more congruent with the host Naga languages. By concentrating on a smaller part of the region spanning three contiguous states of Arunachal Pradesh, Assam and Nagaland from Northeastern India, the findings also help unlock in small ways the mysteries surrounding the diversity of classifiers arising out of areal diffusion.

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Sabbah Qamri. "Dialectal intelligibility of Assamese tested functionally." Indian Journal of Language and Linguistics 2, no. 4 (November 15, 2021): 9–22. http://dx.doi.org/10.54392/ijll2142.

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This paper includes a detailed discussion on the intelligibility of the speakers of four regional dialects of the Indo-Aryan language of Assamese. Prior research on Assamese dialects mostly being confined to examining structural variation lends this study relevance and urgency. The dialects of Standard Assamese, Central Assamese, Kamrupi, and Goalparia, covering three varieties each, were considered for the study. Using a functional intelligibility testing approach, the rate of overall intelligibility as well as of inter- and intra-dialectal mutual intelligibility of the dialects were determined. 24 speakers (1 male and 1 female from each variety) were asked to record ‘texts’— words, sentences, and connected speech in their native varieties of Assamese. 11 listeners from each variety (132 in total) were then tested on their comprehension of texts from non-native varieties. Thereafter, their rates of comprehension were used to determine the rates of mutual intelligibility between speakers of the different dialects and varieties of Assamese. This paper establishes that the rates of mutual intelligibility are unequal and asymmetric among the dialects— the native speakers of the Standard and Central Assamese dialects were more intelligible to the speakers of Kamrupi and Goalparia than vice-versa. Finally, the paper finds that the rate of intelligibility is the lowest for words in isolation and reinforces the important role of context in intelligibility.

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Dev, Chandana, and Amrita Ganguly. "Sentiment Analysis of Review Data: A Deep Learning Approach Using User-Generated Content." Asian Journal of Electrical Sciences 12, no. 2 (November 22, 2023): 28–36. http://dx.doi.org/10.51983/ajes-2023.12.2.4119.

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As information technology progresses rapidly, social media platforms are experiencing exponential growth, accompanied by a surge in online content. Sentiment analysis (SA) of online evaluations has piqued the interest of researchers from various organizations, including academia, government, and private industry. It has become an increasingly hot area of study in the fields of Machine Learning (ML) and natural language processing (NLP). Deep Learning (DL) algorithms are currently being utilized in the same field to achieve remarkable results. Much SA research has been conducted in different languages such as English, Chinese, and Spanish, as well as various Indian languages like Hindi, Malayalam, and Bengali. However, languages like Assamese have received very little attention in this field of research. Hence, this research work provides a novel approach to sentiment analysis by demonstrating the effectiveness of deep neural network models for the less explored and scarce resource language, Assamese. This paper introduces a hybrid model, combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), termed LSTM-CNN, for Sentiment Analysis (SA). Keras word embeddings are employed for vectorization of the data. To achieve outcomes, our proposed models have employed dropout, max pooling, and batch normalization techniques. Experimental analysis is carried out on user-generated review content by translating the Bengali dataset into Assamese. Comparison and evaluation of the built models have been done with traditional machine learning models in predicting sentiment polarity. Comparative analysis shows that all the proposed models outperform with an accuracy of more than 98%.

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Barman, Anup Kumar, Jumi Sarmah, Subungshri Basimatary, and Amitava Nag. "Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation." Engineering, Technology & Applied Science Research 14, no. 1 (February 8, 2024): 12581–86. http://dx.doi.org/10.48084/etasr.6342.

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Word Sense Disambiguation (WSD) is concerned with automatically assigning the appropriate sense to an ambiguous word. WSD is an important task and plays a crucial role in many Natural Language Processing (NLP) applications. A Statistical Machine Translation (SMT) system translates a source into a target language based on phrase-based statistical translation. MT plays a crucial role in a WSD system, as a source language word may be associated with multiple translations in the target language. This study aims to apply WSD to the input of the MT system to enhance the disambiguation output. Hindi WordNet was used by selecting the most frequent synonym to obtain the most accurate translation. This study also compared Naïve Bayes (NB) and Decision Tree (DT) to test and build a WSD model. NB was more appropriate for the WSD task than DT when evaluated in the Weka machine learning toolkit. To the best of our knowledge, no such work has been carried out yet for the Assamese Indo-Aryan language. The applied WSD achieved better results than the baseline MT system without embedding the WSD module. The results were analyzed by linguist scholars. Furthermore, the Assamese-Hindi transliteration system was merged with the baseline MT system for the translation of proper nouns. This study marks a remarkable contribution to Assamese NLP, which is a low computationally aware Indian language.

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Ismail, Tanvira, and L. Joyprakash Singh. "Dialect Identification of Assamese Language using Spectral Features." Indian Journal of Science and Technology 10, no. 20 (February 1, 2017): 1–7. http://dx.doi.org/10.17485/ijst/2017/v10i20/115033.

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Baruah, Dhrubajyoti, and Anjana Kakoti Mahanta. "Design and Development of Soundex for Assamese Language." International Journal of Computer Applications 117, no. 9 (May 20, 2015): 9–12. http://dx.doi.org/10.5120/20581-3000.

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Saikia, Samutjal. "Practice of Writing Science Fiction in Assamese Language: An Introduction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2615–18. http://dx.doi.org/10.31142/ijtsrd15682.

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Goutom, Pritom Jyoti, and Nomi Baruah. "Text Summarization in Assamese Language using Sequence to Sequence RNNs." Indian Journal Of Science And Technology 16, SP2 (December 15, 2023): 22–29. http://dx.doi.org/10.17485/ijst/v16isp2.5429.

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Naik, Dr Vishal, and Heli Mehta. "Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 696–703. http://dx.doi.org/10.22214/ijraset.2023.56079.

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Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.

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Jana, Jyotirmay. "Shakespeare in Comparative Discourse and Influence Studies in the Assamese Language Print Media." Space and Culture, India 2, no. 4 (March 29, 2015): 3. http://dx.doi.org/10.20896/saci.v2i4.128.

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Dr. Sanjib Kuar Baishya. "A Critical Analysis of Adaptation, Domestication and Foreignization as Effective Strategies for Translating Shakespeare’s Plays into Assamese." Creative Launcher 7, no. 6 (December 30, 2022): 75–83. http://dx.doi.org/10.53032/tcl.2022.7.6.08.

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One of the major challenges faced by the translators is finding equivalence in the target language. The translators of Shakespeare plays have used Assamese words as appropriate equivalence of English words used by Shakespeare. However, it is not possible for the translators to claim that a particular kind of translation is the most faithful to the source text or the original text. The critics of translation studies are divided on deciding the parameters to assess whether a particular translation is faithful or not. The translators face various challenges in the process of translation such as finding equivalence, truthfully representing the linguistic and cultural nuances etc. In this process, the Assamese translators of Shakespeare’s plays have used adaptation, domestication, foreignization etc. Although the methods are different, they serve a common purpose, i.e., to bring a culturally and linguistically different text close to Assamese readers. The Comedy of Errors was the first Shakespeare play to be translated into Assamese by Ratnadhar Barua, Ramakanta Barkakoti, Gunjanan Barua and Ghanashyam Barua as Bhramaranga in 1888. Since then, a good number of Shakespeare plays have been either adapted or translated into Assamese. As You Like It, Cymbeline, Macbeth, Troilus and Cressida, Taming of the Shrew, King Lear, A Midsummer Night’s Dream etc. were adapted into Assamese. Romeo and Juliet, Othello, Twelfth Night etc. were translated using domestication as an effective strategy. Othello, Macbeth, Measure for Measure were also translated by other translators using foreignization as an effective strategy. The paper examines the multiple methods that have been used for translation of Shakespeare’s plays into Assamese across time with special emphasis on adaptation, domestication and foreignization. As multiple translations of the same Shakespeare plays are available in Assamese, the paper also highlights the features of those translations and critically comments on their effectiveness in terms of strategies used by the translators. It also underlines the challenges faced by the translators while translating Shakespeare’s plays into Assamese. Specific examples from both the source texts and target texts are given to assess the process of translation. A few translators have retained the original names in the translations. A few others have change the names completely giving some indigenous flavor to the target texts. The choices of the translators and the factors responsible for such choices have also been discussed in this paper. The paper also documents most of the Shakespeare plays translated into Assamese since 1888. However, the assessment of the strategies used to translate the plays is not chronological. The paper is divided into three main parts: ‘Adaptation of Shakespeare’s Plays into Assamese’, ‘Domestication in Translation of Shakespeare’s Plays into Assamese’ and ‘Foreignization in Translation of Shakespeare’s Plays into Assamese’.

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B, Vijayakumar. "Tamil Langauge Learning for Assamese Students: An Error Analysis." International Research Journal of Tamil 2, no. 2 (February 12, 2020): 12–25. http://dx.doi.org/10.34256/irjt2022.

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‘Language learning’ is a continuous process, and the learners have to focus on the various aspects of the language whether it is Tamil, English or any other language. In the process of learning, the learners commit errors while speaking and writing. Through constant practice and proper usage, they gradually avoid errors in their language. It is not possible for a teacher or a learner to get rid of this problem without practicing the language. As far as the Tamil language is concerned, it is learnt as a foreign language in some countries and as a second language in other states of India. The concept of "error" has become one of the major problems in language learning. In this article an attempt is made to examine the important dimensions of Error Analysis, with specific reference to the errors produced by learners of Tamil as a second language at Gauhati University. The paper deals with the scope of Tamil language-learning in Assam and the explanations of different categories of language error committed by students and the solution to the error.

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Rahman, Mehjabeen Suraiya. "Role of Satra & Namghar in the Evolution of Genesis of Assamese Identity." International Journal of Social Sciences and Management 2, no. 2 (April 25, 2015): 108–13. http://dx.doi.org/10.3126/ijssm.v2i2.12143.

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Assam is the home of different ethnic groups with a variety of cultures and speaking different languages and dialects. The population of Assam consists of the inhabitants who migrated into the region at various periods of history from Tibet, Burma, Thailand and Bengal etc. Over time they got integrated as a population and have given birth to the greater Assamese nation. The amalgamated Assamese identity was initiated by the Great Saint Mahapurush Srimanta Sankardeva with his Neo-Vaishnavite Movement. The movement evolved new institutions of Satra and Namghar which began to serve not only as the instrument spreading the faith, but also helped to sustain and to stabilize Vaishnavism by making it a part and parcel of Assamese social and cultural life.Though Neo Vaishnavism was a religious movement but it has defined the culture of Assam & has its bearing on the livelihood. As the doyen of cultural renaissance and harbinger of Bhakti Movement, Sankardeva took on the orthodox elements of the society and introduced cultural initiatives like Bhaonas & Borgeet etc which had in actual defined the Assamese identity With its dynamic philosophy of inclusiveness Sankardeva’s Neo-Vaishnavism has given birth to a new Cultural Nationalism focused on a national identity shaped by cultural traditions and language, not on the concept of common ancestry or race. The Cultural Nationalism was brought forward to the indigenous people with the help of Satras and Namghar which has a major role to play in the preservation and development of the indigenous culture of the region.The paper is an attempt to study the role of the institutions of Neo Vaishnavism, the Satra & Namghar in the evolution of genesis of Assamese identity and its inclusiveness in nation building. The managerial structure and operations of the Satra shall also be explored in the perspective of its position in the modern Assamese Society in the study. The paper shall go in toe area wherein in the genesis of the Assamese Identity, the Namghar is one of the major pole bearers, playing the multi-faceted role of Cultural Centre, Proto-type Panchayat, and Forum for Decentralized Planning and Decision-making.The paper is also an attempt to understand the impact of Neo-Vaisnavism on the Economic Organization of the society along with the role of women and their empowerment for the sustainable development of a progressive & egalitarian Assamese. Key Words- Cultural Renaissance, Inclusiveness, NationDOI: http://dx.doi.org/10.3126/ijssm.v2i2.12143 Int. J. Soc. Sci. Manage. Vol-2, issue-2: 108-113

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Sarmah, Sunita. "Nirupama Borgohain and her novels." Linguistics and Culture Review 6 (March 6, 2022): 529–33. http://dx.doi.org/10.21744/lingcure.v6ns2.2174.

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Nirupama Borgohain is one of the most prominent female voices of Assam. She has contributed more than thirty books to Assamese literature. Her novels are mainly based on realism where she has consciously dealt with the problem of inequality that exist men and women in society. She always highlights the plight of women and their protest against patriarchal values. She is an Indian journalist and novelist in the Assamese Language. She is a Sahitya Akademi Award winner and best known for her novel 'Abhiyatri'. She was a recipient of the Assam Valley Literary Award.

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Bhattacharyya, Archita. "Pronominal in Assamese and Bengali Language: A Comparative Analysis." Space and Culture, India 8, no. 3 (November 29, 2020): 100–108. http://dx.doi.org/10.20896/saci.vi0.936.

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Out of the several modern Indo-Aryan languages that evolved in the eastern part of India, Assamese and Bengali are the two most prominent ones. Though both these two languages reached their respective present existence after passing through different phases of development, yet their roots are the same. Therefore, between both languages, there are many similarities even though both have evolved in distinctly different geographical areas, and there exist distinct differences between them. The differences not only create the distinction between them but also express their individuality too. In both, languages, pronoun and pronominal have occupied an important role in the discussion of morphology. Along with pronoun, the use of various pronominal which have evolved from the same root has flourished in both the languages. In this regard, both similarities and differences could be noticed in these two languages. Therefore, to identify the co-relation as well as the linguistic characteristics of both the languages, the comparative analysis is the only way out. In this study, an attempt is made to focus on how the pronominal of both languages are used to identify the similarities and differences between the two languages.

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Pathak, Guptajit. "An Historical Analysis of Srimanta Sankardeva's Contribution to Language, Literature, and Culture of Assam." Journal of Humanities,Music and Dance, no. 36 (November 19, 2023): 38–47. http://dx.doi.org/10.55529/jhmd.36.38.47.

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The Assamese polymath Srimanta Sankardeva (1449 CE-1568 CE) made a substantial contribution to the development of language, literature and culture in the 15th and 16th centuries of Assam. He was a trailblazer in the fields of education, language, literature, art and culture. Saint Sankaradeva's Neo-Vaishnavite movement is a massive socio-religious and cultural revolution in Assam that plays a significant part in forging close social ties among the state's citizens. Sankardeva's contributions have improved the language, literature, and socio-cultural environment of Assam, however the language has lost some of its original traditions within the Assamese community, for which he most likely supported the Neo-Vaishnavist movement. This is seen in the current cultural setting of Assam, where a plethora of other cultural activities that were inspired by the saint-scholar have erupted and became part of the original customs. Furthermore, several histories of Sankardeva have misrepresented some details of his life.

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Rita Chakraborty. "N-Gram based Assamese Question Pattern Extraction and Probabilistic Modelling." Journal of Electrical Systems 20, no. 3 (April 30, 2024): 712–26. http://dx.doi.org/10.52783/jes.2996.

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N-gram probabilities provide valuable information in understanding, processing, and modelling various natural language processing tasks. They assign probabilities to the sequences of words and subsequently to the whole sentence. Such information is very essential to make more accurate predictions in machine learning based systems. Here in this paper we worked on finding Parts-of-Speech (PoS) sequence based Assamese question patterns. We derived the unique bi-grams and tri-grams of PoSs occurring in the patterns and also extracted the probabilities of them. We then tried to find the unique PoS patterns of Assamese questions. We also have tried to incorporate the probabilities of unique bi-grams and tri-grams and the combined bi-grams and tri-grams probabilities of all patterns. Our work is a novel approach of finding the probabilities of bi-grams and tri-grams of the patterns occurring in Assamese questions.

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Archangeli, Diana B., and Jonathan Yip. "Assamese vowels and vowel harmony." Journal of South Asian Languages and Linguistics 6, no. 2 (February 25, 2020): 151–83. http://dx.doi.org/10.1515/jsall-2019-2010.

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AbstractBased on impressionistic and acoustic data, Assamese is described as having a phonological tongue root harmony system, with blocking by certain phonological configurations and over-application in certain morphological contexts. This study explores physical properties of the patterns using ultrasonic imaging to determine whether the impressionistic descriptions match what speakers actually do. Principal components analysis (PCA) determines that most participants produce a contrast in tongue root position in the appropriate contexts, though there is less of an impact on tongue root with greater distance from the triggering vowel. Analysis uses the root mean squared distance (RMSD) calculation to determine whether both blocking and over-application take effect. The blocking results conform to the impressionistic descriptions. With over-application, [e] and [o] are expected; while some speakers clearly produce these vowels, others articulate a vowel that is indeterminant between the expected [e]/[o] and an unexpected [ɛ]/[ɔ]. No speaker consistently showed the expected tongue root position in all contexts, and some speakers appeared to have lost the contrast entirely, yet all are considered to be speakers of the same dialect of Assamese. Whether this (apparent) loss is a consequence of crude research methodologies or accurately reflects what is happening within the language community remains an open question.

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Patgiri, Chayashree, Mousmita Sarma, and Kandarpa Kumar Sarma. "A Class of Neuro-Computational Methods for Assamese Fricative Classification." Journal of Artificial Intelligence and Soft Computing Research 5, no. 1 (January 1, 2015): 59–70. http://dx.doi.org/10.1515/jaiscr-2015-0019.

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Abstract In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.

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Dattamajumdar, Satarupa. "Ethno-Linguistic Vitality of Koch." Buckingham Journal of Language and Linguistics 12 (December 11, 2020): 55–76. http://dx.doi.org/10.5750/bjll.v12i.1874.

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The Koch language is spoken in the states of Assam (Goalpara, Nagaon, Dhubri, Kokrajhar, Chirang, Bongaigao, Barpeta, Baksa, Udalguri, Karbi Anglong, Golaghat districts), Meghalaya (West Garo Hills, South-West Garo Hills, South Garo Hills and East Khasi Hills Districts). Koches are found in West Bengal (Northern part) and also in Bangladesh. The speaker strength of Koch in India according to 2011 census is 36,434. Koch community is the bilingual speakers of Assamese, Bengali, Garo, Hindi, and English. Contact situations of Koch with Assamese and Bengali languages have made the language vulnerable to language shift. The UNESCO report mentions Koch as ‘Definitely Endangered’1. Koch has gained the status of a scheduled tribe in Meghalaya in 1987. Kondakov (2013) traces six distinct dialects of Koch, viz., Wanang, Koch-Rabha (Kocha), Harigaya, Margan, Chapra and Tintekiya. He (2013:24) states, “The relationship between the six Koch speech varieties are rather complex. They represent a dialect chain that stretches out from Koch-Rabha in the north to Tintekiya Koch in the south.” This is diagrammatically represented as - Koch-Rabha(Kocha)→Wanang→Harigaya→Margan, Chapra→Tintekiya where the adjacent dialects exhibit more lexical similarity than those at the ends. Nine ethno-linguistic varieties of Koch (also mentioned in Kondakov, 2013:5) have been reported during field investigation. These are Harigaya, Wanang, Tintekiya, Margan, Chapra, Satpariya, Sankar, Banai and Koch Mandai.

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Kakoty, Dr Atonu. "LLS use and Proficiency: Assamese ESL Learner’s Context." SMART MOVES JOURNAL IJELLH 8, no. 7 (July 22, 2020): 60–68. http://dx.doi.org/10.24113/ijellh.v8i7.10657.

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This paper studies the relationship of Language Learning Strategies (LLS) use and proficiency. Four hundred and fifty undergraduate Assamese ESL learners from three colleges of Dibrugarh University, Assam participated in the study. The aim of the paper is to investigate the relationship of Assamese ESL Learner’s use of LLS and reading and writing proficiency in English. A Strategy Inventory for Language Learning (SILL) based questionnaire is used to identify learner’s use of LLS and a reading and writing activity questionnaire is used to measure learner’s proficiency. The participants answered the instruments at the beginning of the experiment, followed by LLS instruction for four weeks and a post-test on the same questionnaires. The Pearson’s correlation analysis revealed significant positive correlation between the use of all the six types of LLS categories and learner’s proficiency in reading and writing, both in the pre and post-test context. The study recommends that LLS instruction should be integrated in the undergraduate ESL classrooms to increase learner’s proficiency in English.

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Sharma, Mridusmita, Kandarpa Kumar Sarma, and Nikos E. Mastorakis. "Ethnographically Oriented Repository of Assamese Telephonic Speech." MATEC Web of Conferences 210 (2018): 05019. http://dx.doi.org/10.1051/matecconf/201821005019.

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Recording of the speech samples is the first step in speech recognition and related tasks. For English, there are a bunch of readily available data sets. But standard data sets with regional dialect and mood variations are not available and the need to create our own data set for our experimental works has been faced. We have considered Assamese language for our case study and since it is less computationally aware, there is a need to develop the speech corpus having dialect and mood variations. Also, the development of corpus is an ongoing process and the initial task is reported in this paper.

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Chetia, Gunadeep, and Gopal Chandra Hazarika. "Pre-processing Phase of Automatic Text Summarization for the Assamese Language." International Journal of Computer Sciences and Engineering 6, no. 10 (October 31, 2018): 159–63. http://dx.doi.org/10.26438/ijcse/v6i10.159163.

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Bharali, Sruti Sruba, and Sanjib Kr Kalita. "Speech recognition with reference to Assamese language using novel fusion technique." International Journal of Speech Technology 21, no. 2 (March 23, 2018): 251–63. http://dx.doi.org/10.1007/s10772-018-9501-1.

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SAIKIA, DEEP JYOTI, EISPITA DEY, DHRUBA JYOTI RAJBONGSHI, and Dr PURNENDU BIKASH ACHARJEE. "A REVIEW ON MODELS SPEAKER RECOGNITION WITH RESPECT TO ASSAMESE LANGUAGE." International Journal of Engineering Science and Technology 8, no. 2S (February 28, 2018): 76–78. http://dx.doi.org/10.21817/ijest/2018/v10i2s/181002s010.

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Bora, Jyotishman, Saine Dehingia, Abhijit Boruah, Anuraag Anuj Chetia, and Dikhit Gogoi. "Real-time Assamese Sign Language Recognition using MediaPipe and Deep Learning." Procedia Computer Science 218 (2023): 1384–93. http://dx.doi.org/10.1016/j.procs.2023.01.117.

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Sarma, Surajit, and Nabankur Pathak. "Design and Implementation of an Assamese Language Chatbot Using Neural Networks." International Journal of Scientific Research in Computer Science and Engineering 11, no. 6 (December 31, 2023): 13–18. http://dx.doi.org/10.26438/ijsrcse/v11i6.1318.

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Konch, Hemanta. "Nominal Inflection of the Tutsa Language." International Journal of Innovative Technology and Exploring Engineering 10, no. 4 (February 28, 2021): 138–40. http://dx.doi.org/10.35940/ijitee.d8428.0210421.

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North-East is a hub of many ethnic languages. This region constitutes with eight major districts; like-Assam, Arunachal Pradesh, Nagaland, Manipur, Mizoram, Tripura, Meghalaya and Sikkim. Tutsa is a minor tribe of Arunachal Pradesh. The Tutsa was migrated from the place ‘RangkhanSanchik’ of the South-East Asia through ‘Hakmen-Haksan’ way to Arunachal Pradesh. The Tutsa community is mainly inhabited in Tirap district and southern part of Changlang district and a few people are co-exists in Tinsukia district of Assam. The Tutsa language belongs to the Naga group of Sino-Tibetan language family. According to the Report of UNESCO, the Tutsa language is in endangered level and it included in the EGIDS Level 6B. The language has no written literature; songs, folk tales, stories are found in a colloquial form. They use Roman Script. Due to the influence of other languages it causes lack of sincerity for the use of their languages in a united form. Now-a-days the new generation is attracted for using English, Hindi and Assamese language. No study is found till now in a scientific way about the language. So, in this prospect the topic Nominal Inflection of the Tutsa Language has been selected for study. It will help to preserve the language and also help in making of dictionary, Grammar and language guide book.

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Talukdar, Kuwali, Shikhar Kumar Sarma, Farha Naznin, and Ratul Deka. "Deep Learning based UPoS Tagger for Assamese Religious Text." International Journal of Religion 5, no. 4 (March 27, 2024): 163–70. http://dx.doi.org/10.61707/nn1dfz44.

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Religious texts are known to be with specific patterns of writing, and also involve specific vocabularies. These are also known to follow specific style of writing. Thereby these texts are enriched with typical semantic and syntactic characteristics, demanding special attention for Natural Language Processing (NLP) tasks. This research paper focuses on the application of Deep Learning (DL) techniques for Parts of Speech (PoS) tagging focusing on Assamese language religious texts. We have created a specialized dataset comprising approximately 11,000 sentences extracted from various sources including web crawling and filtering religious texts from existing corpora. The dataset was manually validated by linguists to ensure accuracy, errors, and corrections required. A performance matrix was constructed to analyze the performance of the initial tagging using a pre-existing DL-based model trained for Assamese Universal Parts of Speech (UPoS) tagger. Following this, we utilized a subset of the dataset for manual evaluation, and the validated dataset is then considered as a gold standard training dataset for training other DL models using GRU, RNN and Bidirectional LSTM (BiLSTM) architectures. Training accuracies were recorded and presented, demonstrating the effectiveness of the proposed approach. Accuracies, Precision, and Recall were recorded for all the three Models. F1 scores also have been calculated. Comparison of training and testing accuracies are depicted with performance graphs.

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Buragohain, Dipima. "Tracing the “extinctness” of Tai Ahom: issues of language loss and death." International Journal of the Sociology of Language 2018, no. 252 (June 26, 2018): 163–77. http://dx.doi.org/10.1515/ijsl-2018-0020.

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Abstract The extinction of Tai Ahom, a language from the Tai-Kadai family that was once spoken in Assam, India, can be contributed to a number of socio-historical, linguistic and cultural factors including the development and strong influence of Assamese, a language from the Indo-Aryan family spoken in that region. In an effort to examine the dynamics that contributed to the loss of Tai Ahom, the current article focuses on a descriptive overview of issues including language shift, variation, and extinction in the case of Tai Ahom. It will also highlight the factors which contributed to its shift and gradual extinction and discuss current efforts to revive the language.

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Sarma, Himangshu, Navanath Saharia, and Utpal Sharma. "Development and Analysis of Speech Recognition Systems for Assamese Language Using HTK." ACM Transactions on Asian and Low-Resource Language Information Processing 17, no. 1 (November 16, 2017): 1–14. http://dx.doi.org/10.1145/3137055.

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Phukan, Rituraj, Nomi Baruah, Shikhar Kr Sarma, and Darpanjit Konwar. "Exploring Character-Level Deep Learning Models for POS Tagging in Assamese Language." Procedia Computer Science 235 (2024): 1467–76. http://dx.doi.org/10.1016/j.procs.2024.04.138.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/871324.

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In spoken word recognition, one of the crucial points is to identify the vowel phonemes. This paper describes an Artificial Neural Network (ANN) based algorithm developed for the segmentation and recognition of the vowel phonemes of Assamese language from some words containing those vowels. Self-Organizing Map (SOM) trained with a various number of iterations is used to segment the word into its constituent phonemes. Later, Probabilistic Neural Network (PNN) trained with clean vowel phonemes is used to recognize the vowel segment from the six different SOM segmented phonemes. One of the important aspects of the proposed algorithm is that it proves the validation of the recognized vowel by checking its first formant frequency. The first formant frequency of all the Assamese vowels is predetermined by estimating pole or formant location from the linear prediction (LP) model of the vocal tract. The proposed algorithm shows a high recognition performance in comparison to the conventional Discrete Wavelet Transform (DWT) based segmentation.

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Ghosh, Koyel, Apurbalal Senapati, Mwnthai Narzary, and Maharaj Brahma. "Hate Speech Detection in Low-Resource Bodo and Assamese Texts with ML-DL and BERT Models." Scalable Computing: Practice and Experience 24, no. 4 (November 17, 2023): 941–55. http://dx.doi.org/10.12694/scpe.v24i4.2469.

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Hate speech detection research is a recent sizzling topic in natural language processing (NLP). Unburdened uses of social media platforms make people over-opinionative, which crosses the limit of leaving comments and posts toxic. A toxic outlook increases violence towards the neighbour, state, country, and continent. Several laws have been introduced in different countries to end the emergency problem. Now, all the media platforms have started working on restricting hate posts or comments. Hate speech detection is generally a text classification problem if considered a supervised observation. To tackle text in terms of computation perspective is challenging because of its semantic and complex grammatical nature. Resource-rich languages leverage their richness, whereas resource scarce language suffers significantly from a lack of dataset. This paper makes a multifaceted contribution encompassing resource generation, experimentation with Machine Learning (ML), Deep Learning (DL) and state-of-the-art transformer-based models, and a comprehensive evaluation of model performance, including thorough error analysis. In the realm of resource generation, it adds to the North-East Indian Hate Speech tagged dataset (NEIHS version 1), which encompasses two languages: Assamese and Bodo.

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E J Honesty Praiselin, Dr. G Manikandan, Vilma Veronica, and Ms. S. Hemalatha. "Sign Language Detection and Recognition Using Media Pipe and Deep Learning Algorithm." International Journal of Scientific Research in Science and Technology 11, no. 2 (April 2, 2024): 123–30. http://dx.doi.org/10.32628/ijsrst52411223.

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People lacking the sense of hearing and the ability to speak have undeniable communication problems in their life. People with hearing and speech problems communicate using sign language with themselves and others. These communicating signs are made up of the shape of the hand and movement. Sign language is not essentially known to a more significant portion of the human population who uses spoken and written language for communication. Therefore, it is a necessity to develop technological tools for interpretation of sign language. Much research have been carried out to acknowledge sign language using technology for most global languages. But there are still scopes of development of tools and techniques for sign language development for local dialects. This work attempts to develop a technical approach for recognizing American Sign Language Using machine learning techniques, this work tried to establish a system for identifying the hand gestures from American Sign Language. A combination of two-dimensional and three-dimensional images of Assamese gestures has been used to prepare a dataset. The Media Pipe framework has been implemented to detect landmarks in the images. The results reveal that the method implemented in this work is effective for the recognition of the other alphabets and gestures in Sign Language. This method could also be tried and tested for the recognition of signs and gestures for various other local languages of India

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Sarma, Parismita, and S. K. Sarma. "Syllable based approach for text to speech synthesis of Assamese language: A review." Journal of Physics: Conference Series 1706 (December 2020): 012168. http://dx.doi.org/10.1088/1742-6596/1706/1/012168.

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Bhuyan, M. P., and S. K. Sarma. "An N-gram based model for predicting of word-formation in Assamese language." Journal of Information and Optimization Sciences 40, no. 2 (February 17, 2019): 427–40. http://dx.doi.org/10.1080/02522667.2019.1580883.

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Journal articles on the topic 'Assamese language'

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