1 Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan.
2 (Basic Doctoral Program) at the National University of Uzbekistan.
International Journal of Science and Research Archive, 2025, 16(01), 1539-1545
Article DOI: 10.30574/ijsra.2025.16.1.2141
Received on 09 June 2025; revised on 19 July 2025; accepted on 21 July 2025
This article presents a description of language models that take into account the unique features of the Uzbek language. Language modeling plays a significant role in improving the accuracy and performance of Automatic Speech Recognition (ASR) systems. Enhancing the conversion of speech to text can be achieved by correctly identifying syntactic and semantic structures in continuous speech. To achieve this goal, statistical and neural network-based language models, including deep learning architectures such as n-grams, Recurrent Neural Networks (RNNs), and transformer models, have been utilized.
Automatic Speech Recognition (ASR); Language Model; Uzbek Speech; N-Gramm; Syntactic-Statistical Model; Neural Network Model; Uzbek Language Trigram Model; Hidden Markov Models (Hmms)
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Narzillo Mamatov Solidjonovich, Nurbek Nuritdinov Davlataliyevich and Muxiyatdinov Jamalatdin Kayratdin ulı. Neural network-based modeling for continuous speech recognition in the Uzbek Language. International Journal of Science and Research Archive, 2025, 16(01), 1539-1545. Article DOI: https://doi.org/10.30574/ijsra.2025.16.1.2141.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
