The relevance of the problem of automatic speech recognition lies in the lack of research for low-resource languages, stemming from limited training data and the necessity for new technologies to enhance efficiency and performance. The purpose of this work was to study the main aspects of integrated end-to-end speech recognition and the use of modern technologies in the natural processing of agglutinative languages, including Kazakh. In this article, the study of language models was carried out using comparative, graphic, statistical, and analytical-synthetic methods, which were used in combination. This article addresses automatic speech recognition (ASR) in agglutinative languages, particularly Kazakh, through a unified neural network model that integrates both acoustic and language modeling. Employing advanced techniques like connectionist temporal classification and attention mechanisms, the study focuses on effective speech-to-text transcription for languages with complex morphologies. Transfer learning from high-resource languages helps mitigate data scarcity in languages such as Kazakh, Kyrgyz, Uzbek, Turkish, and Azerbaijani. The research assesses model performance, underscores ASR challenges, and proposes advancements for these languages. It includes a comparative analysis of phonetic and word-formation features in agglutinative Turkic languages, using statistical data. The findings aid further research in linguistics and technology for enhancing speech recognition and synthesis, contributing to voice identification and automation processes.

languages, due to its agglutinative morphology and ergative-absolutive alignment. Agglutinative morphology refers to the fact that it is a language that Basque ( BASK, BAHSK; endonym euskara [eus̺ˈkaɾa]) is a language spoken by Basques and other residents of the Basque Country, a region that straddles the westernmost Pyrenees in adjacent parts of southwestern France and northern Spain. Basque is the only known language isolate (with no relation to any other known languages) in all of Europe. The Basques are indigenous to and primarily inhabit the Spanish did not fully shift /f/ to /h/; instead, it has preserved /f/ before consonants such as /w/ and /ɾ/ (cf fuerte, frente). (On the other hand, the occurrence of [f] in those words might be a secondary development from an earlier sound such as [h] or [ɸ] and learned words or words influenced by written Latin form. Gascon has /h/ in these words, which might reflect the original situation.) Evidence of Arabic loanwords in Spanish points to /f/ continuing to exist long after a Basque substrate might have had any effect on Spanish. (On the other hand, the occurrence of /f/ in those words might be a late development. Many languages have come to accept new phonemes from other languages after a period of significant influence. For example, French lost /h/ but later regained it as a result of…

A resource-based Korean morphological annotation system We describe a resource-based method of morphological annotation of written Korean text. Korean is an agglutinative language. The output of our system is a graph of morphemes annotated with accurate linguistic information. The language resources used by the system can be easily updated, which allows us-ers to control the evolution of the per-formances of the system. We show that morphological annotation of Korean text can be performed directly with a lexicon of words and without morpho-logical rules. Published as: Dans Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP) - A resource-based Korean morphological annotation system, Jeju : Cor\'ee, R\'epublique de (2005) arXiv categories: cs.CL

intelligible speech varieties, or dialect continuum, that have no traditional name in common, and which may be considered distinct languages by their speakers Malay (UK: mə-LAY; endonym: Bahasa Melayu, Jawi script: بهاس ملايو) is an Austronesian language native to several islands of Maritime Southeast Asia and the Malay Peninsula on mainland Asia. The language is an official language of Brunei, Malaysia, and Singapore, where the standardised forms are known as Standard Malay. Within the Malay language family, another standardised form which is known as Malay is an agglutinative language, and new words are formed by three methods: attaching affixes onto a root word (affixation), formation of a compound word (composition), or repetition of words or portions of words (reduplication). Nouns and verbs may be basic roots, but frequently they are derived from other words by means of prefixes, suffixes and circumfixes. Malay does not make use of grammatical gender, and there are only a few words that use natural gender; the same word is used for 'he' and 'she' which is dia or for 'his' and 'her' which is dia punya. There is no grammatical plural in Malay either; thus orang may mean either 'person' or 'people'. Verbs are not inflected for person or number, and they are not marked for tense; tense is instead denoted by time adverbs (such as 'yesterday') or by other tense indicators, such as sudah 'already' and belum 'not yet'. On the other hand, there is a complex system of verb affixes to render nuances of meaning and to denote voice or intentional and accidental moods. Malay does not have a grammatical subject in the sense that English does. In intransitive clauses, the noun comes before the verb. When there is both an agent and an object, these are separated by the verb (OVA or AVO), with the difference encoded in the voice of the verb. OVA, commonly but inaccurately called "passive", is the basic and most common word order.

measure to the way in which the two have been handled respectively. The immensely comprehensive order of agglutinative languages is sometimes reduced

compare the general structure of two languages , one out of each family. The simple possession in common of an agglutinative character, as thus defined, would

Grammatical error correction (GEC) is crucial for enhancing the readability and comprehension of texts, particularly in improving text quality in low-resource languages. However, challenges such as data scarcity, linguistic diversity, and limited computational resources hinder advancements in this domain. To address these challenges, researchers have developed strategies such as synthetic data generation, multilingual pre-trained models, and cross-lingual transfer learning. This review synthesizes findings from key studies to explore effective GEC methods for low-resource languages, emphasizing approaches for handling limited annotated corpora, typological complexities, and evaluation challenges. Synthetic data generation techniques, including noise injection, adversarial error generation, and translationese-based augmentation, have proven vital for overcoming data scarcity. Multilingual and transfer learning approaches demonstrate effectiveness in adapting knowledge from high-resource languages to low-resource settings, especially when combined with fine-tuning on curated datasets. Additionally, linguistic diversity has been partially addressed through methods like morphology-aware embeddings, byte-level tokenization, and contextual data preprocessing. However, limited research exists on robust evaluation metrics tailored to diverse typologies, such as agglutinative and morphologically rich languages, and the creation of gold-standard datasets remains an ongoing challenge. Recent advancements in dataset construction and the use of large language models further enrich this field, offering scalable solutions for low-resource contexts. Despite notable progress, this review identifies gaps in evaluation methodologies and typology-specific solutions, calling for future innovations in multilingual modeling, dataset creation, and computationally efficient GEC systems tailored to the unique needs of low-resource languages.