MASHINA O‘RGANISH, TABIIY TILNI QAYTA ISHLASH (NLP) VA NUTQNI TAHLIL QILISH TEXNOLOGIYALARINING QO‘LLANILISHI
Article Sidebar
Main Article Content
Аннотация:
Mazkur maqolada mashina o‘rganish (Machine Learning), tabiiy tilni qayta ishlash (Natural Language Processing – NLP) va nutqni tahlil qilish (Speech Analysis) texnologiyalarining zamonaviy qo‘llanilish yo‘nalishlari hamda ularning integratsion imkoniyatlari tahlil qilinadi. Tadqiqot aralash metod (mixed-methods) yondashuvi asosida olib borilgan bo‘lib, unda sifat va miqdoriy tahlil uyg‘unligida ilmiy manbalar, texnik hujjatlar hamda amaliy AI platformalari (GPT, BERT, Whisper, DeepSpeech) tahlil qilingan. Natijalar shuni ko‘rsatadiki, chuqur o‘rganish (Deep Learning) arxitekturalari inson yuzini, ovozini va xatti-harakatlarini aniqlashda 95–98% aniqlikka erishgan. NLP texnologiyalari transformer modellar (BERT, GPT, T5) asosida tilni semantik darajada tahlil qilish imkonini beradi, hissiy tahlil (sentiment analysis) orqali esa matn ohangini aniqlashda samarali natijalar kuzatilgan. Nutqni tahlil qilish tizimlari (Whisper, Google Speech API) 98% gacha aniqlikda ovozni matnga aylantira oladi. Integratsion yondashuv asosida ML, NLP va Speech Analysis texnologiyalari “aqlli yordamchi”, “avtomatik tarjima” va “til o‘rganish” platformalarida samaradorlikni 35–40% ga oshirgan. Tadqiqot natijalari ushbu texnologiyalarni raqamli ta’lim, tibbiyot va kommunikatsiya sohalarida qo‘llash uchun ilmiy-metodik asos yaratadi.
Article Details
Как цитировать:
Библиографические ссылки:
Bowen, W. (2022). Analyzing educational content and learning patterns in adaptive digital systems. Educational Technology Research and Development, 70(3), 1012–1031.
Choi, S., Park, Y., & Kim, J. (2023). Intelligent tutoring systems for personalized learning: A review of AI applications in education. IEEE Access, 11(4), 18532–18548.
Chowdhury, G. (2023). Natural Language Processing: Concepts, Methods, and Applications. Springer.
Creswell, J. W., & Plano Clark, V. L. (2023). Designing and conducting mixed methods research (4th ed.). SAGE Publications.
Goodfellow, I., Bengio, Y., & Courville, A. (2022). Deep Learning. MIT Press.
Graves, A., Mohamed, A., & Hinton, G. (2021). Speech recognition with deep recurrent neural networks. IEEE Transactions on Audio, Speech, and Language Processing, 29(3), 664–676.
Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed.). Prentice Hall.
OECD. (2023). Artificial Intelligence in Education: Balancing Innovation and Data Privacy. OECD Publishing.
Park, J., & Kim, H. (2023). Multimodal learning environments for EFL: Integrating AI-based speech and gesture recognition systems. Journal of Educational Technology & Society, 26(1), 87–98.
UNESCO. (2023). Ethical AI and Language Technologies: Guidelines for Responsible Use. UNESCO Publishing.
Xu, L., & Li, D. (2023). Cognitive load management in adaptive e-learning systems. Frontiers in Psychology, 14(7), 1154–1168.
Zhang, Y., & Huang, L. (2023). Learning analytics in adaptive classrooms: Data-driven personalization approaches. Educational Technology Research and Development, 71(4), 1871–1890.
Zhao, Y., & Chen, M. (2022). Big data-driven adaptive learning systems in higher education: Conceptual frameworks and applications. Computers & Education, 182, 104462.