Аннотация
This paper presents an AI-driven methodology for constructing parallel text corpora and synthesizing parallel audio datasets for low-resource Turkic language pairs, namely Kazakh-Kyrgyz and Kazakh-Uzbek. The scarcity of high-quality linguistic and speech resources for these languages poses significant challenges for the development of neural machine translation and automatic speech recognition systems. The paper proposes and validates a methodology for parallel dataset construction driven by artificial intelligence, in which the selection of a translation system is guided by a set of criteria encompassing free accessibility, translation quality, and processing efficiency, assessed through experiments on a 2000-sentence test set. Based on this evaluation, large-scale parallel corpora for the Kazakh–Kyrgyz and Kazakh–Uzbek language pairs were generated using the selected AI system. A subsequent manual error analysis revealed that approximately 3.7% (Kazakh–Kyrgyz) and 4.3% (Kazakh–Uzbek) of the translations contained inaccuracies, indicating the need for post-editing and further model refinement. Audio synthesis experiments using MMS-TTS and TurkicTTS systems demonstrated that synthetic speech of near-natural quality can be generated for both languages, with NISQA scores reaching up to 4.44 for Uzbek. The findings confirm that the proposed methodology provides a practical and scalable foundation for expanding linguistic resources for low-resource Turkic languages and supporting further research in machine translation and speech processing.