Relevant bibliographies by topics / Assamese language

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Assamese language'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "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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Dissertations / Theses on the topic "Assamese language"

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ISLAM, ZAHIRUL. "HANDWRITTEN CHARACTER RECOGNITION OF ASSAMESE HANDWRITTEN RECOGNITION USING CONVOLUTION NEURAL NETWORK." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19249.

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The involvement of technology is reshaping our perception of the world. The inherent desire to understand human consciousness and intelligence has led to the widespread development of the fields like Artificial Intelligence and Machine learning. [1] Artificial intelligence has paved its way into most disciplines and blended in as an essential tool to boost efficiency and non-conventional enhancements. Linguistics is one such field; with the involvement of AI, communication and text extraction have become vividly easier. The presented work involves the development of one such application: Handwritten Text Recognition for the Assamese language. The presented work analyses text extraction from images and understanding by classifying it into proper categories for machines to understand it using the Assamese language, which is spoken in the Indian state of Assam. The framework of the work can easily be utilized for other languages just by scanning or capturing the text of the mentioned language. In this Project, the use of convolution neural networks(CNNs) is analyzed and proposed as the feature extractor for the handwritten Assamese characters. The classification for successful recognition of the scripts is achieved using the final layer of the CNN as a softmax activation layer. The dataset is obtained from the UCI repository for the training and testing of the proposed model. The results achieved by the testing of the model are quite satisfactory, with an accuracy of 99.87%.
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Books on the topic "Assamese language"

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Medhi, Kaliram. Assamese grammar and origin of the Assamese language. Guwahati: Publication Board, Assam, 1988.

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editor, Pāṭagirī Jagadīśa, and Project of History of Indian Science, Philosophy, and Culture. Sub Project: Consciousness, Science, Society, Value, and Yoga, eds. Assamese language, literature and culture. New Delhi: Project of History of Indian Science, Philosophy and Culture, Sub-Project: Consciousness, Science, Society, Value, and Yoga, Centre for Studies in Civilizations, 2018.

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Chaliha, Makhan Lal. English-Assamese dictionary. New Delhi: Asian Educational Services, 2002.

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Baruah, P. N. Dutta. Assamese phonetic reader. Mysore: Central Institute of Indian Languages, 1992.

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William, Robinson. A grammar of the Assamese language. Dibrugarh: Dept. of Assamese, Dibrugarh University, 1996.

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North Eastern Language Society (Gauhati, India), ed. The INEL Assamese primer. Guwahati: North Eastern Language Society, 1986.

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Borah, Gautam K. Aspects of modern Assamese. Guwahati: Bhabani Books in association with Department of English and Foreign Languages, Tezpur University, Napaam, Tezpur, 2016.

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Dattabaruwā, Muṇīndra Nārāẏaṇa. Asama hitaishīra ṭokābahīra parā: Svargīẏa Munīndra Nārāẏana Dattabaruwāra likhanirājira kiẏadāṃśa. Guwāhāṭī: Munīndra Nārāẏana Dattabaruwāra Pariẏāla Barga, 2000.

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Baruah, P. N. Dutta. Sān̐ko =: An intermediate course reader in Assamese. Mysore: Central Institute of Indian Languages, Ministry of Human Resource and Development (Dept. of Education), Govt. of India, 1999.

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Dewī, Bhāmatī, and Central Institute of Indian Languages., eds. Prajñā =: An advanced course reader in Assamese. Mahāśura: Bhāratīẏa Bhāshā Saṃsthāna, 1996.

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Book chapters on the topic "Assamese language"

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Cantlie, Audrey. "The Language of Food." In The Assamese, 182–220. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003331278-8.

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Borah, Pranjal Protim, Gitimoni Talukdar, and Arup Baruah. "WSD for Assamese Language." In Advances in Intelligent Systems and Computing, 119–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1280-9_11.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Sounds of Assamese Language." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 77–93. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_4.

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Saikia, Pori, and Marc Allassonnière-Tang. "Chapter 3. Nominal classification in Assamese." In Nominal Classification in Asia and Oceania, 30–55. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/cilt.362.03sai.

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We provide an analysis of the classifier system in Assamese (Indo-European) via the framework of functional typology. Assamese is located at the border of Indo-European and Sino-Tibetan language families, which are typically associated with grammatical gender and classifiers, respectively. Assamese represents an insightful example of an Indo-European language relying on classifiers rather than grammatical gender to fulfill the functions typical for a nominal classification system. Our analysis shows that classifiers in Assamese behave similarly to other classifier languages in terms of lexical and discourse functions, except for the functions of definiteness marking and individuation. The implications of such findings are connected to typology, research in human cognition, and language contact.
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Roy, Arindam, Sunita Sarkar, and Hsubhas Borkakoty. "A Lemmatizer Tool for Assamese Language." In Communications in Computer and Information Science, 124–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8581-0_10.

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Goswami, Indira. "The Immortality of the Assamese Language." In Indira Goswami, 59–60. London: Routledge India, 2022. http://dx.doi.org/10.4324/9781003147015-18.

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Choudhury, Ranjan, Nabamita Deb, and Kishore Kashyap. "Context-Sensitive Spelling Checker for Assamese Language." In Advances in Intelligent Systems and Computing, 177–88. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1280-9_18.

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Das, Mridusmita, and Apurbalal Senapati. "COVID-19 Neologism in the Assamese Language." In Artificial Intelligence and Data Science Based R&D Interventions, 9–18. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2609-1_2.

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Medhi, Smriti Priya, and Shikhar Kumar Sarma. "Authorship Attribution for Assamese Language Documents: Initial Results." In Communications in Computer and Information Science, 232–42. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47224-4_21.

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Deka, R. R., S. Kalita, M. P. Bhuyan, and S. K. Sarma. "A Study of Various Natural Language Processing Works for Assamese Language." In Learning and Analytics in Intelligent Systems, 128–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42363-6_15.

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Conference papers on the topic "Assamese language"

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Chakraborty, Joyshree, Shikhamoni Nath, Nirmala S. R, and Samudravijaya K. "Language Identification of Assamese, Bengali and English Speech." In The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/sltu.2018-37.

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Deka, Barsha, Nirmala S.R., and Samudravijaya K. "Development of Assamese Continuous Speech Recognition System." In The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/sltu.2018-45.

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Talukdar, Gitimoni, Pranjal Protim Borah, and Arup Baruah. "Supervised named entity recognition in Assamese language." In 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014. http://dx.doi.org/10.1109/ic3i.2014.7019728.

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Borkakoty, Hsuvas, and Utpal Sharma. "A Naive Extractive Text Summarizer for Assamese Language." In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2021. http://dx.doi.org/10.1109/icirca51532.2021.9544769.

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Roy, Nibedita, and Apurbalal Senapati. "An Overview of the Basic NLP Resources Towards Building the Assamese-English Machine Translation." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.7.

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Machine Translation (MT) is the process of automatically converting one natural language into another, preserving the exact meaning of the input text to the output text. It is one of the classical problems in the Natural Language Processing (NLP) domain and there is a wide application in our daily life. Though the research in MT in English and some other language is relatively in an advanced stage, but for most of the languages, it is far from the human-level performance in the translation task. From the computational point of view, for MT a lot of preprocessing and basic NLP tools and resources are needed. This study gives an overview of the available basic NLP resources in the context of Assamese-English machine translation.
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Basu, Tulika, Arup Saha, and Somnath Chandra. "Objective verification of Assamese consonants." In 2015 International Conference Oriental COCOSDA held jointly with 2015 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE). IEEE, 2015. http://dx.doi.org/10.1109/icsda.2015.7357879.

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Baruah, Nomi, Arjun Gogoi, Rituraj Phukan, and Pritom Jyoti Goutom. "Hate Speech Detection for a low level language (Assamese)." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10307931.

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Sharma, Padmaja, Utpal Sharma, and Jugal Kalita. "Named entity recognition in Assamese using CRFS and rules." In 2014 International Conference on Asian Language Processing (IALP). IEEE, 2014. http://dx.doi.org/10.1109/ialp.2014.6973498.

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Baishya, Diganta, and Pradip K. Das. "Improving windows tasks recognizer for Assamese using bigram analysis." In 2014 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2014. http://dx.doi.org/10.1109/icalip.2014.7009838.

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Singh, Moirangthem Tiken, Partha Pratim Barman, and Rupjyoti Gogoi. "Speech Recognition Model for Assamese Language Using Deep Neural Network." In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE). IEEE, 2018. http://dx.doi.org/10.1109/icrieece44171.2018.9008668.

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