SUN’IY INTELLEKT TEXNOLOGIYALARI ASOSIDA TEZ YORDAM QOʻNGʻIROQLARINI QAYTA ISHLASH TIZIMI
DOI:
https://doi.org/10.47390/ts-v4i3y2026N01Ключевые слова:
tabiiy tilni qayta ishlash, nutqni matnga aylantirish, whisper modeli, nomlangan ma’lumotlarni aniqlash, ma’lumotlarni strukturalash.Аннотация
Ushbu maqolada tez yordam dispetcherlik xizmatlari uchun sun’iy intellekt texnologiyalari yordamida qo‘ng‘iroqlarni tahlil qilish tizimi taqdim etilgan. Tizim asosi sifatida FeruzaSpeech va Uzbek Speech Corpus tanlanmalaridagi 121378 ta namunada fine-tuning qilingan Whisper-Small modeli va gibrid NER algoritmlari qo‘llanilgan. Tadqiqot natijasida nutqni tanib olishda 14.7% WER ko‘rsatkichiga erishildi, tibbiy simptomlar, manzillar va yosh ko‘rsatkichlari 88% aniqlikda ajratib olindi. Tizimning 1.8 soniyalik ishlov berish tezligi uni real vaqt rejimida qo‘llash imkonini beradi. Mazkur yechim dispetcherlar ish yukini kamaytirish va tibbiy yordam ko‘rsatish tezligini oshirishga xizmat qiladi.
Библиографические ссылки
1. Afraa Attiah, Manal Kalkatawi. AI-powered smart emergency services support for 9-1-1 call handlers using textual features and SVM model for digital health optimization. Frontiers in Big Data 8:1594062, 2025.
2. B.Sagar, A.Roopashree, M.Keerthana, G.Shanmugharatinam. Medical Emergency Handling Using AI. International Journal of Scientific Research in Engineering and Management 09(05):1-6, 2025.Neal Mahajan, Erik Holzwanger, Jeremy Glissen Brown, Tyler M. Berzin. Deploying automated machine learning for computer vision projects: a brief introduction for endoscopists. VideoGIE 8(6):249-251, 2023.
3. Gakire Munyaneza. The Role of AI in Enhancing Emergency Medical Services. Eurasian Experiment Journal of Biological Sciences 5(2):17-20, 2024.
4. Saranya, K. and Jeevananthan, P. A Real-Time Speech-Text Conversion System Using Deep Learning Technique. In Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence, pp. 371-388, 2025.
5. C.Wang, Y.Wu, Y.Du, et.al. Semantic Mask for Transformer Based End-to-End Speech Recognition. Proceedings Interspeech 2020:971-975, 2020.
6. Mirosław Płaza, Łukasz Pawlik, Stanisław Deniziak. Call Transcription Methodology for Contact Center Systems. IEEE Access 9:110988, 2021.
7. Warto, S. Rustad, G.F. Shidik, E. Noersasongko, Purwanto, Muljono, I.M. Setiadi. Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application. Statistics, Optimization & Information Computing 12(4):907-942, 2024.
8. Ahmad Fauzan Zaky, Chanifah Indah Ratnasari. Developing Rule-Based and AI Hybrid Chatbot for Academic Information Services. International Journal of Informatics and Computation 7(2):755-768, 2025.
9. Ryan Fernando, Chamara Disanayake, Chamindra Attanayake. Voice Driven AI Based Automated VoIP PBX for SMEs. International Conference on Advanced Computing Technologies, Sri Lanka, 2025, pp. 139-146.
10. Quentin Meeus,Marie-Francine Moens, Hugo Van Hamme. Whisper-SLU: Extending a Pretrained Speech-to-Text Transformer for Low Resource Spoken Language Understanding. 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Taipei, Taiwan, 2023, pp. 1-6.
11. Shengnan Zhang, Yan Hu, Guangrong Bian. Research on string similarity algorithm based on Levenshtein Distance. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2017, pp. 2247-2251.
12. Shahzad Qaiser, Ramsha Ali. Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. International Journal of Computer Applications 181(1):25-29, 2018.
13. R.Pavithra Devi, K.Shesathri, Y.Madhumitha, U.Dinesh, A.Mohanty. A Scalable Approach to Fake News Detection Using TF-IDF and Logistic Regression. 2025 International Conference on Sensors and Related Networks (SENNET), Vellore, India, 2025, pp. 1-6.
14. Anna Povey, Katherine Povey. FeruzaSpeech: A 60 Hour Uzbek Read Speech Corpus with Punctuation, Casing, and Context. Proceedings of the 7th International Conference on Natural Language and Speech Processing, Trento, Italy, 2024, pp. 360-364.
15. M.Musaev, S.Mussakhojayeva, I.Khujayorov, Y.Khassanov, M.Ochilov, H.A.Varol. USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments. 23rd International Conference on Speech and Computer, St. Petersburg, Russia, 2021, pp. 437-447.
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