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"Kazakh language Texts"
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The Development and Experimental Evaluation of a Multilingual Speech Corpus for Low-Resource Turkic Languages
The development of parallel audio corpora for Turkic languages, such as Kazakh, Uzbek, and Tatar, remains a significant challenge in the development of multilingual speech synthesis, recognition systems, and machine translation. These languages are low-resource in speech technologies, lacking sufficiently large audio datasets with aligned transcriptions that are crucial for modern recognition, synthesis, and understanding systems. This article presents the development and experimental evaluation of a speech corpus focused on Turkic languages, intended for use in speech synthesis and automatic translation tasks. The primary objective is to create parallel audio corpora using a cascade generation method, which combines artificial intelligence and text-to-speech (TTS) technologies to generate both audio and text, and to evaluate the quality and suitability of the generated data. To evaluate the quality of synthesized speech, metrics measuring naturalness, intonation, expressiveness, and linguistic adequacy were applied. As a result, a multimodal (Kazakh–Turkish, Kazakh–Tatar, Kazakh–Uzbek) corpus was created, combining high-quality natural Kazakh audio with transcription and translation, along with synthetic audio in Turkish, Tatar, and Uzbek. These corpora offer a unique resource for speech and text processing research, enabling the integration of ASR, MT, TTS, and speech-to-speech translation (STS).
Journal Article
Religious Texts of the Khotons of Mongolia: “Garvaa Gorvoo”
The purpose of this article is driven by the need to interpret linguistic facts found in the folklore of particular people in order to archive their extinct language. To achieve this goal, a task was set to study one of the religious texts of the Khotons – a small Turkic ethnic group living in Mongolia – which has been preserved orally. Using a comparative-historical research design and the methods of internal and external reconstruction, morphological structures within the sampled text were studied and loanwords identified. Variation A of Garvaa Gorvoo in the Khoton language acted as the primary source for this study. a distributive analysis was conducted encompassing both semantic and phonological features of the Mongolian language and the text written in Mongolian Cyrillic, which greatly resembled the Khoton language of the sampled text. For the first time, the unique winding speech style of the Khoton ethnic group in Mongolia was analyzed, translated into Kazakh and English, and its content revealed. The results reveal that due to the close connection of religious texts with Arabic, Garvaa Gorvoo was determined by the meaning of the Arabic word ġurbat (غربة) or gharbā; hence proving the association with the Arabic language. This analysis also revealed grammatical forms and previously undocumented lexical items. The results of this study contribute to a deeper understanding of the historical and cultural identity of the Khotons in Mongolia. Furthermore, they help determine the phonetic and lexico-grammatical characteristics of their spoken language, revealing its connections to Turkic languages, and open the way to archiving the language by presenting a \"living fact\" of this extinct language to the international scholarly community.
Journal Article
Classification and Translation of Generic Names in Literary Texts
In the modern world, the problem of translating generic names in literary texts is becoming increasingly important. This article was devoted to the analysis of various aspects of the translation of gender terms from one language to another in literary texts. The authors reviewed theoretical approaches to the translation of generic names and identified the main problems that translators face when conveying such terms. They also examined various strategies for translating gender titles, pronouns, appearances, and behaviors, taking culturally specific aspects into consideration. The translation of Kazakh literary texts was analyzed in three directions: biological, social, and psychological. As a result, not only the incorrectness but also the advantages of direct translation were revealed. The importance of context when translating gender terms and the need to take into account the language and culture of the source and target text were also discussed. The authors drew conclusions about the importance of translating gender names correctly and appropriately in a literary context.
Journal Article
The Significance of the Lexical-Grammatical Minimum in Teaching Kazakh at the A2 Level
This study explores the methodological effectiveness of an LGM-based textbook designed for A2-level learners. The relevance of the research stems from the shortage of systematic, standardized educational resources despite the growing demand for Kazakh language acquisition. To address this gap, the study analyzes the practical effectiveness of a textbook developed on the basis of LGM structure and content. The objective of the research is to assess the content, structure, and methodological features of the A2-level LGM-based textbook and to evaluate its effectiveness through a comparative analysis with an alternative textbook. The study employs methods such as comparative analysis, experimentation, surveys, and both qualitative and quantitative analyses. The research was conducted in language training centers across the Akmola, Kostanay, North Kazakhstan, and Karaganda regions, as well as in the city of Astana, involving 14 instructors and 179 learners. The findings indicate that the structure of the LGM-based textbook is systematic, its content is accessible, and its exercises are fully aligned with the learners’ proficiency level. Assignments oriented towards communicative situations were found to have a positive impact on the development of core language skills, including speaking, writing, reading, and listening. The results of the study contribute theoretically to the advancement of language teaching methodologies and practically to the design and implementation of effective educational materials. Furthermore, the outcomes emphasize the critical role of the LGM framework in Kazakh language instruction and lay the groundwork for future initiatives in its standardization and systematization.
Journal Article
The neural machine translation models for the low-resource Kazakh–English language pair
The development of the machine translation field was driven by people’s need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages by artificially increasing the size of the corpora and improving the performance of the Kazakh–English machine translation models. These methods are called forward translation, backward translation, and transfer learning. Then the Sequence-to-Sequence (recurrent neural network and bidirectional recurrent neural network) and Transformer neural machine translation architectures with their features and specifications are concerned for conducting experiments in training models on parallel corpora. The experimental part focuses on building translation models for the high-quality translation of formal social, political, and scientific texts with the synthetic parallel sentences from existing monolingual data in the Kazakh language using the forward translation approach and combining them with the parallel corpora parsed from the official government websites. The total corpora of 380,000 parallel Kazakh–English sentences are trained on the recurrent neural network, bidirectional recurrent neural network, and Transformer models of the OpenNMT framework. The quality of the trained model is evaluated with the BLEU, WER, and TER metrics. Moreover, the sample translations were also analyzed. The RNN and BRNN models showed a more precise translation than the Transformer model. The Byte-Pair Encoding tokenization technique showed better metrics scores and translation than the word tokenization technique. The Bidirectional recurrent neural network with the Byte-Pair Encoding technique showed the best performance with 0.49 BLEU, 0.51 WER, and 0.45 TER.
Journal Article