BUILDING A THEORETICALLY INFORMED UZBEK-ENGLISH SPEECH RECOGNITION SYSTEM: PRACTICAL IMPLEMENTATION AND EVALUATION

Abstract

This paper presents the development and evaluation of a theoretically informed Uzbek-English speech recognition system, aimed at enhancing bilingual natural language processing capabilities for Central Asian linguistic environments. The study integrates linguistic theory, acoustic modeling, and neural network-based recognition techniques to improve accuracy and cross-lingual adaptability. Using hybrid deep learning architectures, the system was trained on a mixed corpus of Uzbek and English speech data. Experimental results indicate a significant reduction in word error rate (WER) compared to baseline monolingual models. The paper concludes by discussing the implications for multilingual AI development and future prospects for low-resource language technologies.