Nomeri 14 (2026): Markaziy Osiyoda media va kommunikatsiyalar Xalqaro ilmiy jurnali
Maqolalar

NUTQ FONEMALARINING ARTIKULYATSION VA VIZUAL TASVIRLARI: ANIMATSIYADA SINXRONIZATSIYA MASALALARI

Gulshan Kayumova
O‘zbekiston jurnalistika va ommaviy kommunikatsiyalar universiteti
Munojat Sultonova
Toshkent axborot texnologiyalari universiteti

Nashr qilingan 2026-10-04

Kalit so‘zlar

  • fonema,
  • artikulyatsiya,
  • undosh tovush,
  • unli tovush,
  • portlovchi tovush,
  • frikativ,
  • affrikat,
  • lateral,
  • burun tovush,
  • nutq animatsiyasi,
  • vizual nutq,
  • sinxronizatsiya
  • ...Ko'proq
    Kamroq

Izoh

Ushbu maqolada nutq fonemalarining artikulyatsion, akustik va vizual xususiyatlari, shuningdek, ularni animatsiyada ifodalash masalalari tahlil qilinadi. Undosh va unli fonemalar, portlovchi, frikativ, affrikat, lateral, titroq va burun tovushlari tasniflanadi. Qahramonning lab harakati va yuz mushaklarining nutq bilan sinxronlashtirilishi jarayoni, fonemalarning vizual ifodalarini tayyorlash va animatsiya kadrlariga joylashtirishning pedagogik va texnologik ahamiyati muhokama qilinadi. Tadqiqot natijalari nutq animatsiyasi, interaktiv ta’lim vositalari va sun’iy intellektga asoslangan tizimlarda qo‘llanilishi mumkinligini ko‘rsatadi

 

 

 

 

Bibliografik manbalar

  1. Avriel, M. (2003). Nonlinear Programming: Analysis and Methods. M. Avriel. Courier Corporation. 512 p. ISBN: 978-0-486-43227-4. Retrieved September 06, 2026 from https://books.google.co.uz/books?id=byF4Xb1QbvMC&printsec=frontcover&hl=ru#v=onepage&q&f=false
  2. Bressem, Jana & Ladewig, Silva. (2011). Rethinking gesture phases: Articulatory features of gestural movement?. Semiotica. 184. 53–91. DOI: 10.1515/semi.2011.022. Retrieved September 06, 2026 from https://philpapers.org/rec/BRERGP?utm_source
  3. Cao, C., et al. (2013). Facewarehouse: A 3D facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics, 20(3), 413–425. DOI:10.1109/TVCG.2013.249. https://pubmed.ncbi.nlm.nih.gov/24434222/
  4. Beknazarova S., Sadullaeva S., Bazhenov R., Qayumova G., Jaumitbayeva M. Application of nonlinear splitting algorithm to the method of reference equations. AIP Conf. Proc. 16 June 2022; 2432 (1): 060003. https://doi.org/10.1063/5.0089494
  5. Beknazarova S., Yunusova D., Qayumova G. et al. Adaptive video compression and transmission algorithms for smart surveillance in IOT networks, Proc. SPIE 14014, Advanced Materials for Optics and Photonics: Chemistry and Engineering Perspectives (AMOP 2025), 1401403 (18 Dec 2025); https://doi.org/10.1117/12.3091984
  6. Cudeiro, D. et al. (2019). Capture, Learning, and Synthesis of 3D Speaking Styles. Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). - С. 10101-10111. Retrieved September 06, 2026 from http://voca.is.tue.mpg.de/.
  7. Graves, A., et al. (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd International Conference on Machine Learning (pp. 369–376). DOI: 10.1145/1143844.1143891. Retrieved September 06, 2026 from https://mlanthology.org/icml/2006/graves2006icml-connectionist/?utm_source
  8. Kendon, A. (2004). Gesture: Visible Action as Utterance. Cambridge University Press. Retrieved September 06, 2026 from 10.1017/CBO9780511807572.
  9. Nguyen, N. (2000). Perceiving Talking Faces: From Speech Perception to a Behavioral Principle by Massaro, D. W. Journal of Phonetics, 28(1), 103–109. DOI: 10.1006/jpho.2000.0108.
  10. Pelachaud, C. (2009). Studies on gesture expressivity for a virtual agent. Speech Communication, 51(7), 630–639. https://doi.org/10.1016/j.specom.2008.04.009
  11. Pham, H. X., Pavlovic, V., Cai, J., & Cham, T. (2016). Robust real-time performance-driven 3D face tracking. In L. Davis, A. Del Bimbo, & B. C. Lovell (Eds.), 2016 23rd International Conference on Pattern Recognition (ICPR 2016) (pp. 1851-1856). Article 7899906 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPR.2016.7899906
  12. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.. (2011) ORB: An efficient alternative to SIFT or SUFT. ICCV ‘11 Proceedings of the 2011 International Conference on Computer Vision. – P. 2564–2571. DOI: 10.1109/ICCV.2011.6126544. https://ieeexplore.ieee.org/document/6126544?utm_source
  13. Taylor, S., Kim, T., Yue, Y., Mahler, M., Krahe, J., Rodriguez, A., ... & Matthews, I. (2020). A deep learning approach for generalized speech animation. ACM Transactions on Graphics, 39(4), 93:1–93:15. https://doi.org/10.1145/3386569.3392450
  14. Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31(3), 279–311. DOI: https://doi.org/10.1007/BF02289464
  15. Wagner, Petra & Malisz, Zofia & Kopp, Stefan. (2014). Gesture and speech in interaction: An overview. Speech Communication. 57. 209-232. Retrieved September 06, 2026 from 10.1016/j.specom.2013.09.008.
  16. Kayumova, G. & Boymurodov, B. (2025). Uch o‘lchamli personajlarning yuz holatini modellashtirish va animatsiyalashning zamonaviy yondashuvlari. Al-Farg’oniy avlodlari, 1 (3), 151-158. doi: 10.5281/zenodo.17295640
  17. Корзун В.А. (2022). Генерация мимики для виртуальных ассистентов. Труды Московского физико-технического института, 14(3/55), 57–62. URL: https://sciup.org/generacija-mimiki-dlja-virtualnyh-assistentov-142236478?utm_source