O‘ZBEK TILI UCHUN SUN’IY INTELLEKT ASOSIDA UZLUKSIZ NUTQNI TANISH TIZIMINI YARATISH: KORPUS, AKUSTIK MODEL VA TIL MODELINI LOYIHALASH

Mualliflar

  • Husan Arziqulov

Kalit so'zlar

https://doi.org/10.47390/ts-v4i3y2026N04

Kalit so'zlar

sun’iy intellekt; o‘zbek tili; avtomatik nutqni tanish; nutq korpusi; akustik model; til modeli; chuqur o‘rganish; CTC - diqqat; WER; CER.

Annotasiya

Ushbu maqolada o‘zbek tili uchun sun’iy intellekt asosidagi uzluksiz nutqni tanishtirishning ilmiy asoslari tahlili. Tadqiqot tekshiriladigan birlamchi manbalarga tayangan holda korpusdan, ma’lumotlarni oldindan qayta ishlash, akustik modellashtirish, til modelini qurish, dekodlash va uni tikishni loyihalashtirish tizimlarini ravshan umumlashtirish. Metodologik asos sifatida qiyosiy tahlil, ishlab chiqarish va e’lon qilingan tahlilni sharhlash usullaridan foydalanildi. O‘zbek tilidagi nutqni tanish sifati, avvalo, korpusning sifati va hajmi, preprocessing intizomi, gibrid chuqur o‘qitish arxitekturalari, o‘zbek tilining agglutinativ xususiyatga moslashtirilgan til modellari bilan bog‘liq. Bu loyiha asosida, preprocessing, belgilovchi, akustik modellashtirish, til modelini qurish va xatolikni yaratish bilan dekodlashdan olti bosqichli arxitektura taklif.

Manbalar

1. Russell, S., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

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3. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

4. Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press.

5. Musaev, M., Mussakhojayeva, S., Khujayorov, I., Khassanov, Y., Ochilov, M., & Varol, H. A. (2021). USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments. In A. Karpov & R. Potapova (Eds.), Speech and Computer, SPECOM 2021, Lecture Notes in Computer Science, Vol. 12997 (pp. 437–447). Springer. https://doi.org/10.1007/978-3-030-87802-3_40

6. Musaev, M., Khujayorov, I., & Ochilov, M. (2020). Development of integral model of speech recognition system for Uzbek language. In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1–6). IEEE. https://doi.org/10.1109/AICT50176.2020.9368719

7. Mukhamadiyev, A., Khujayarov, I., Djuraev, O., & Cho, J. (2022). Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language. Sensors, 22(10), 3683. https://doi.org/10.3390/s22103683

8. Mukhamadiyev, A., Mukhiddinov, M., Khujayarov, I., Ochilov, M., & Cho, J. (2023). Development of Language Models for Continuous Uzbek Speech Recognition System. Sensors, 23(3), 1145. https://doi.org/10.3390/s23031145

9. Madrakhimov, S., Makharov, K., & Lolaev, M. (2021). Data preprocessing on input. AIP Conference Proceedings, 2365(1), 030003. https://doi.org/10.1063/5.0058132

10. Zaynidinov, H., & Mallayev, O. (2022). Parallel algorithm for calculating the learning processes of an artificial neural network. AIP Conference Proceedings, 2647(1), 050006. https://doi.org/10.1063/5.0104178

11. Musaev, M., Khujayarov, I., & Ochilov, M. (2023). Speech Recognition Technologies Based on Artificial Intelligence Algorithms. In H. Zaynidinov, M. Singh, U. S. Tiwary, & D. Singh (Eds.), Intelligent Human Computer Interaction, IHCI 2022, Lecture Notes in Computer Science, Vol. 13741 (pp. 51–62). Springer. https://doi.org/10.1007/978-3-031-27199-1_6

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Yuborilgan

2026-03-25

Nashr qilingan

2026-03-25

Qanday ko'rsatish

Arziqulov, H. (2026). O‘ZBEK TILI UCHUN SUN’IY INTELLEKT ASOSIDA UZLUKSIZ NUTQNI TANISH TIZIMINI YARATISH: KORPUS, AKUSTIK MODEL VA TIL MODELINI LOYIHALASH. Techscience Uz - Topical Issues of Technical Sciences, 4(3), 25–31. https://doi.org/10.47390/ts-v4i3y2026N04

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