Інформаційна система озвучення україномовного тексту на основі методів NLP та машинного навчання

dc.citation.epage22
dc.citation.issue14
dc.citation.journalTitleВісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі
dc.citation.spage1
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationЛьвівський національний університет імені Івана Франка
dc.contributor.affiliationУніверситет Оснабрюка
dc.contributor.affiliationЖитомирський державний університет імені Івана Франка
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.affiliationIvan Franko National University of Lviv
dc.contributor.affiliationOsnabrück University
dc.contributor.affiliationZhytomyr Ivan Franko State University
dc.contributor.authorБєлоусов, Ілля
dc.contributor.authorЧирун, Любомир
dc.contributor.authorЧирун, Софія
dc.contributor.authorБудз, Ігор
dc.contributor.authorВласенко, Ольга
dc.contributor.authorBielousov, Illia
dc.contributor.authorChyrun, Lyubomyr
dc.contributor.authorChyrun, Sofia
dc.contributor.authorBudz, Ihor
dc.contributor.authorVlasenko, Olha
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-09-12T07:21:49Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractПід час дослідження розроблено інформаційну систему озвучення україномовного тексту на основі методів NLP та машинного навчання. Створена інформаційна система реалізована у виг- ляді десктоп-додатка, який дає змогу здійснювати озвучення україномовного тексту Створення системи охоплювало всі стадії розроблення програмного забезпечення: процес проєктування, процес реалізації та процес тестування. Щоб обґрунтувати доцільність створення такої системи, ми проаналізували вже наявні програмні рішення на ринку, їхні переваги та недоліки, які ураховували, створюючи нову систему. Під час системного аналізу системи сформовано дерево цілей, дерево рішень та наведено приклади контекстних діаграм із декомпозицією процесів. Одним із етапів оформлення економічної частини, де проаналізовано бюджет, потрібний для реалізації системи, розраховано усі витрати на податки та адміністративні витрати, також проаналізовано стратегії розвитку та вибрано стратегію розвитку продукту із супутніми рішеннями та стратегію розвитку продукту. Після цього надано оцінку доцільності створення проєктованої системи, її окупності та прибутку. Об’єкт дослідження – процес системи озвучення україномовного тексту на основі методів NLP та машинного навчання. Предмет дослідження – методи та засоби процесу системи озвучення україномовного тексту на основі методів NLP та машинного навчання. Метою дослідження є створення інформаційної системи озвучення україномовного тексту на основі методів NLP та машинного навчання. Результат роботи – готова до реалізації інформаційна система озвучення україномовного тексту на основі методів NLP та машинного навчання. Здійснено аналітичний огляд літературних та онлайн-джерел, що стосуються теми озвучення україномовного тексту на основі методів NLP та машинного навчання, системний аналіз об’єкта дослідження, аналіз та вибір програмних засобів для реалізації системи, практичну реалізацію системи, економічне обґрунтування діяльності впровадження систем.
dc.description.abstractDuring the research, an information system for voicing Ukrainian-language text was developed based on NLP and machine learning methods. The created information system is implemented in the form of a desktop application, which allows the process of voicing the Ukrainian-language text. The created system included all stages of software development: the design process, the implementation process, and the testing process. For the feasibility of creating this system, already existing software solutions on the market were analysed, their advantages and disadvantages were listed, which were subsequently taken into account to create a new system. During the system analysis of the system, a goal tree, a decision tree, and examples of context diagrams with process decomposition are given. One of the stages of the design of the economic part, where the budget that will be spent on the implementation of the system is analysed, all tax and administrative costs are calculated, development strategies are also analysed and the development strategy of the existing product with accompanying solutions and the product development strategy are selected. After that, an assessment was made for the feasibility of creating the designed system, it’s payback and profit. The object of the research is the process of the voiceover system of the Ukrainian-language text based on NLP and machine learning methods. The subject of the research is the methods and means of the Ukrainian-language text voicing system process based on NLP and machine learning methods. The purpose of the research is to create an information system for voicing Ukrainian- language text based on NLP and machine learning methods. The result of the work is a ready-to- implement information system for voicing Ukrainian-language text based on NLP and machine learning methods, an analytical review of literary and online sources related to the topic of voicing Ukrainian- language text based on NLP and machine learning methods, a systematic analysis of the research object, analysis and selection of software tools for system implementation, practical implementation of the system, economic justification of system implementation activities.
dc.format.extent1-22
dc.format.pages22
dc.identifier.citationІнформаційна система озвучення україномовного тексту на основі методів NLP та машинного навчання / Ілля Бєлоусов, Любомир Чирун, Софія Чирун, Ігор Будз, Ольга Власенко // Вісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі. — Львів : Видавництво Львівської політехніки, 2023. — № 14. — С. 1–22.
dc.identifier.citationenInformation system for ukrainian text voiceover based on NLP and machine learning methods / Illia Bielousov, Lyubomyr Chyrun, Sofia Chyrun, Ihor Budz, Olha Vlasenko // Information Systems and Networks. — Lviv : Lviv Politechnic Publishing House, 2023. — No 14. — P. 1–22.
dc.identifier.doidoi.org/10.23939/sisn2023.14.001
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/111702
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі, 14, 2023
dc.relation.ispartofInformation Systems and Networks, 14, 2023
dc.relation.references1. Shieldt, G. C. The complete reference. NY: Osborne McGraw-Hill, 1989.
dc.relation.references2. Martin, Robert C. Clean code: a handbook of agile software craftsmanship. Pearson Education, 2009.
dc.relation.references3. De Micheli, Giovanni, Rolf Ernst, and Wayne Wolf. Readings in hardware/software co-design. Morgan Kaufmann, 2002.
dc.relation.references4. Karan, B., Mahto, K., & Sahu, S. S. (2019). Intelligent Speech Processing in the Time-Frequency Domain. In Intelligent Speech Signal Processing, 153–173. Academic Press. https://doi.org/10.1016/B978-0-12-818130-0.00009-X
dc.relation.references5. Senior, Andrew W., and Anthony Robinson (1995). “Forward-backward retraining of recurrent neural networks”. Advances in Neural Information Processing Systems, 8.
dc.relation.references6. Richter, Jeffrey. CLR via c#. Vol. 4. Redmond: Microsoft Press, 2006.
dc.relation.references7. Nagel, Christian. Professional C# and. Net. John Wiley & Sons, 2021.
dc.relation.references8. Matthew Mcdonald. Pro WPF 4.5 in C#: Windows Presentation Foundation in .NET 4.5, 2012.
dc.relation.references9. Van Santen, J. P., Sproat, R., Olive, J., & Hirschberg, J. (Eds.). (2013). Progress in speech synthesis. Springer Science & Business Media.
dc.relation.references10. Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. J. (2019). A review of deep learning based speech synthesis. Applied Sciences, 9(19), 4050. https://doi.org/10.3390/app9194050
dc.relation.references11. Schroeder, M. R. (1993). A brief history of synthetic speech. Speech communication, 13(1–2), 231–237. https://doi.org/10.1016/0167-6393(93)90074-U
dc.relation.references12. Klatt, D. H. (1987). Review of text-to-speech conversion for English. The Journal of the Acoustical Society of America, 82(3), 737–793. https://doi.org/10.1121/1.395275
dc.relation.references13. Flanagan, James L. (2013). Speech analysis synthesis and perception. Springer Science & Business Media, Vol. 3.
dc.relation.references14. Isewon, I., Oyelade, J., & Oladipupo, O. (2014). Design and implementation of text to speech conversion for visually impaired people. International Journal of Applied Information Systems, 7(2), 25–30. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ed030474fe850f1c0ab4e8005af00e0671d14ffa
dc.relation.references15. Whitchurch, G. G., & Constantine, L. L. (1993). Systems theory. In Sourcebook of family theories and methods: A contextual approach, 325–355. Boston, MA: Springer US. https://doi.org/10.1007/978-0-387-85764-0_14
dc.relation.references16. Anderson, Brian DO, and Sumeth Vongpanitlerd. Network analysis and synthesis: a modern systems theory approach. Courier Corporation, 2013.
dc.relation.references17. Cronholm, Stefan. Why CASE Tools in Information Systems Development?: An Empirical Study Concerning Motives for Investing in CASE Tools. Linköping University, Department of Computer and Information Science, 1995.
dc.relation.references18. Whitman, L., Huff, B., & Presley, A. (1997, December). Structured models and dynamic systems analysis: the integration of the IDEF0/IDEF3 modeling methods and discrete event simulation. In Proceedings of the 29th conference on Winter simulation, 518–524. https://dl.acm.org/doi/pdf/10.1145/268437.268559
dc.relation.references19. Bublyk, M., Kalynii, T., Varava, L., Vysotska, V., Chyrun, L., & Matseliukh, Y. (2022). Decision Support System Design For Low-Voice Emergency Medical Calls At Smart City Based On Chatbot Management In Social Networks. Webology (ISSN: 1735-188X), 19(2).
dc.relation.references20. Dokhnyak, B., & Vysotska, V. (2021). Intelligent Smart Home System Using Amazon Alexa Tools. In MoMLeT+ DS, CEUR workshop proceedings, 441–464. https://ceur-ws.org/Vol-2917/paper33.pdf
dc.relation.references21. Vysotska, V., Holoshchuk, S., & Holoshchuk, R. (2021). A Comparative Analysis for English and Ukrainian Texts Processing Based on Semantics and Syntax Approach. In COLINS, CEUR workshop proceedings, 311–356. https://ceur-ws.org/Vol-2870/paper26.pdf
dc.relation.references22. Aksonov, D., Gozhyj, A., Kalinina, I., & Vysotska, V. (2021, September). Question-Answering Systems Development Based on Big Data Analysis. In 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), Vol. 1, 113–118. IEEE. DOI: 10.1109/CSIT52700.2021.9648631
dc.relation.references23. Lytvyn, V., Sharonova, N., Hamon, T., Vysotska, V., Grabar, N., & Kowalska-Styczen, A. (2018). Computational linguistics and intelligent systems. In CEUR workshop proceedings, Vol. 2136. https://ceur-ws.org/Vol-3171/preface.pdf
dc.relation.references24. Lytvyn, V., Vysotska, V., Mykhailyshyn, V., Peleshchak, I., Peleshchak, R., & Kohut, I. (2019, July). Intelligent system of a smart house. In 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), 282–287. IEEE. DOI: 10.1109/AIACT.2019.8847748
dc.relation.references25. Voloshyn, S., Vysotska, V., Markiv, O., Dyyak, I., Budz, I., & Schuchmann, V. (2022, November). Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool. In 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 83–88. IEEE. DOI: 10.1109/CSIT56902.2022.10000627
dc.relation.references26. Kravets, P., Burov, Y., Oborska, O., Vysotska, V., Dzyubyk, L., & Lytvyn, V. (2021). Stochastic Game Model of Data Clustering. In IntelITSIS, CEUR workshop proceedings, 198–213. https://ceur-ws.org/Vol-2853/paper19.pdf
dc.relation.references27. Shakhovska, N., Vysotska, V., & Chyrun, L. (2017). Intelligent systems design of distance learning realization for modern youth promotion and involvement in independent scientific researches. In Advances in Intelligent Systems and Computing: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2016, September 6–10 Lviv, Ukraine, 175–198. Springer International Publishing. https://doi.org/10.1007/978-3-319-45991-2_12
dc.relation.references28. Lytvyn, V., et. al. (2019, September). A smart home system development. In Conference on Computer Science and Information Technologies, 804–830. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33695-0_54
dc.relation.references29. Vysotska, V., Markiv, O., Teslia, S., Romanova, Y., & Pihulechko, I. (2022). Correlation Analysis of Text Author Identification Results Based on N-Grams Frequency Distribution in Ukrainian Scientific and Technical Articles. In CEUR Workshop Proceedings, Vol. 3171, 277–314. CEUR-WS. https://ceur-ws.org/Vol-3171/paper25.pdf
dc.relation.references30. Tymoshenko, K., Vysotska, V., Kovtun, O. V., Holoshchuk, R., & Holoshchuk, S. (2021). Real-Time Ukrainian Text Recognition and Voicing. In COLINS, CEUR workshop proceedings, 357–387. https://ceur-ws.org/Vol-2870/paper27.pdf
dc.relation.referencesen1. Shieldt, G. C. The complete reference. NY: Osborne McGraw-Hill, 1989.
dc.relation.referencesen2. Martin, Robert C. Clean code: a handbook of agile software craftsmanship. Pearson Education, 2009.
dc.relation.referencesen3. De Micheli, Giovanni, Rolf Ernst, and Wayne Wolf. Readings in hardware/software co-design. Morgan Kaufmann, 2002.
dc.relation.referencesen4. Karan, B., Mahto, K., & Sahu, S. S. (2019). Intelligent Speech Processing in the Time-Frequency Domain. In Intelligent Speech Signal Processing, 153–173. Academic Press. https://doi.org/10.1016/B978-0-12-818130-0.00009-X
dc.relation.referencesen5. Senior, Andrew W., and Anthony Robinson (1995). "Forward-backward retraining of recurrent neural networks". Advances in Neural Information Processing Systems, 8.
dc.relation.referencesen6. Richter, Jeffrey. CLR via c#. Vol. 4. Redmond: Microsoft Press, 2006.
dc.relation.referencesen7. Nagel, Christian. Professional C# and. Net. John Wiley & Sons, 2021.
dc.relation.referencesen8. Matthew Mcdonald. Pro WPF 4.5 in C#: Windows Presentation Foundation in .NET 4.5, 2012.
dc.relation.referencesen9. Van Santen, J. P., Sproat, R., Olive, J., & Hirschberg, J. (Eds.). (2013). Progress in speech synthesis. Springer Science & Business Media.
dc.relation.referencesen10. Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. J. (2019). A review of deep learning based speech synthesis. Applied Sciences, 9(19), 4050. https://doi.org/10.3390/app9194050
dc.relation.referencesen11. Schroeder, M. R. (1993). A brief history of synthetic speech. Speech communication, 13(1–2), 231–237. https://doi.org/10.1016/0167-6393(93)90074-U
dc.relation.referencesen12. Klatt, D. H. (1987). Review of text-to-speech conversion for English. The Journal of the Acoustical Society of America, 82(3), 737–793. https://doi.org/10.1121/1.395275
dc.relation.referencesen13. Flanagan, James L. (2013). Speech analysis synthesis and perception. Springer Science & Business Media, Vol. 3.
dc.relation.referencesen14. Isewon, I., Oyelade, J., & Oladipupo, O. (2014). Design and implementation of text to speech conversion for visually impaired people. International Journal of Applied Information Systems, 7(2), 25–30. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ed030474fe850f1c0ab4e8005af00e0671d14ffa
dc.relation.referencesen15. Whitchurch, G. G., & Constantine, L. L. (1993). Systems theory. In Sourcebook of family theories and methods: A contextual approach, 325–355. Boston, MA: Springer US. https://doi.org/10.1007/978-0-387-85764-0_14
dc.relation.referencesen16. Anderson, Brian DO, and Sumeth Vongpanitlerd. Network analysis and synthesis: a modern systems theory approach. Courier Corporation, 2013.
dc.relation.referencesen17. Cronholm, Stefan. Why CASE Tools in Information Systems Development?: An Empirical Study Concerning Motives for Investing in CASE Tools. Linköping University, Department of Computer and Information Science, 1995.
dc.relation.referencesen18. Whitman, L., Huff, B., & Presley, A. (1997, December). Structured models and dynamic systems analysis: the integration of the IDEF0/IDEF3 modeling methods and discrete event simulation. In Proceedings of the 29th conference on Winter simulation, 518–524. https://dl.acm.org/doi/pdf/10.1145/268437.268559
dc.relation.referencesen19. Bublyk, M., Kalynii, T., Varava, L., Vysotska, V., Chyrun, L., & Matseliukh, Y. (2022). Decision Support System Design For Low-Voice Emergency Medical Calls At Smart City Based On Chatbot Management In Social Networks. Webology (ISSN: 1735-188X), 19(2).
dc.relation.referencesen20. Dokhnyak, B., & Vysotska, V. (2021). Intelligent Smart Home System Using Amazon Alexa Tools. In MoMLeT+ DS, CEUR workshop proceedings, 441–464. https://ceur-ws.org/Vol-2917/paper33.pdf
dc.relation.referencesen21. Vysotska, V., Holoshchuk, S., & Holoshchuk, R. (2021). A Comparative Analysis for English and Ukrainian Texts Processing Based on Semantics and Syntax Approach. In COLINS, CEUR workshop proceedings, 311–356. https://ceur-ws.org/Vol-2870/paper26.pdf
dc.relation.referencesen22. Aksonov, D., Gozhyj, A., Kalinina, I., & Vysotska, V. (2021, September). Question-Answering Systems Development Based on Big Data Analysis. In 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), Vol. 1, 113–118. IEEE. DOI: 10.1109/CSIT52700.2021.9648631
dc.relation.referencesen23. Lytvyn, V., Sharonova, N., Hamon, T., Vysotska, V., Grabar, N., & Kowalska-Styczen, A. (2018). Computational linguistics and intelligent systems. In CEUR workshop proceedings, Vol. 2136. https://ceur-ws.org/Vol-3171/preface.pdf
dc.relation.referencesen24. Lytvyn, V., Vysotska, V., Mykhailyshyn, V., Peleshchak, I., Peleshchak, R., & Kohut, I. (2019, July). Intelligent system of a smart house. In 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), 282–287. IEEE. DOI: 10.1109/AIACT.2019.8847748
dc.relation.referencesen25. Voloshyn, S., Vysotska, V., Markiv, O., Dyyak, I., Budz, I., & Schuchmann, V. (2022, November). Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool. In 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 83–88. IEEE. DOI: 10.1109/CSIT56902.2022.10000627
dc.relation.referencesen26. Kravets, P., Burov, Y., Oborska, O., Vysotska, V., Dzyubyk, L., & Lytvyn, V. (2021). Stochastic Game Model of Data Clustering. In IntelITSIS, CEUR workshop proceedings, 198–213. https://ceur-ws.org/Vol-2853/paper19.pdf
dc.relation.referencesen27. Shakhovska, N., Vysotska, V., & Chyrun, L. (2017). Intelligent systems design of distance learning realization for modern youth promotion and involvement in independent scientific researches. In Advances in Intelligent Systems and Computing: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2016, September 6–10 Lviv, Ukraine, 175–198. Springer International Publishing. https://doi.org/10.1007/978-3-319-45991-2_12
dc.relation.referencesen28. Lytvyn, V., et. al. (2019, September). A smart home system development. In Conference on Computer Science and Information Technologies, 804–830. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33695-0_54
dc.relation.referencesen29. Vysotska, V., Markiv, O., Teslia, S., Romanova, Y., & Pihulechko, I. (2022). Correlation Analysis of Text Author Identification Results Based on N-Grams Frequency Distribution in Ukrainian Scientific and Technical Articles. In CEUR Workshop Proceedings, Vol. 3171, 277–314. CEUR-WS. https://ceur-ws.org/Vol-3171/paper25.pdf
dc.relation.referencesen30. Tymoshenko, K., Vysotska, V., Kovtun, O. V., Holoshchuk, R., & Holoshchuk, S. (2021). Real-Time Ukrainian Text Recognition and Voicing. In COLINS, CEUR workshop proceedings, 357–387. https://ceur-ws.org/Vol-2870/paper27.pdf
dc.relation.urihttps://doi.org/10.1016/B978-0-12-818130-0.00009-X
dc.relation.urihttps://doi.org/10.3390/app9194050
dc.relation.urihttps://doi.org/10.1016/0167-6393(93)90074-U
dc.relation.urihttps://doi.org/10.1121/1.395275
dc.relation.urihttps://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ed030474fe850f1c0ab4e8005af00e0671d14ffa
dc.relation.urihttps://doi.org/10.1007/978-0-387-85764-0_14
dc.relation.urihttps://dl.acm.org/doi/pdf/10.1145/268437.268559
dc.relation.urihttps://ceur-ws.org/Vol-2917/paper33.pdf
dc.relation.urihttps://ceur-ws.org/Vol-2870/paper26.pdf
dc.relation.urihttps://ceur-ws.org/Vol-3171/preface.pdf
dc.relation.urihttps://ceur-ws.org/Vol-2853/paper19.pdf
dc.relation.urihttps://doi.org/10.1007/978-3-319-45991-2_12
dc.relation.urihttps://doi.org/10.1007/978-3-030-33695-0_54
dc.relation.urihttps://ceur-ws.org/Vol-3171/paper25.pdf
dc.relation.urihttps://ceur-ws.org/Vol-2870/paper27.pdf
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.rights.holder© Бєлоусов І. С., Чирун Л. В., Чирун С. Л., Будз І. С., Власенко О. М., 2023
dc.subjectінформаційна система
dc.subjectNLP
dc.subjectукраїномовний текст
dc.subjectмашинне навчання
dc.subjectозвучення тексту
dc.subjectinformation system
dc.subjectNLP
dc.subjectUkrainian-language text
dc.subjectmachine learning
dc.subjecttext voiceover
dc.subject.udc004.9
dc.titleІнформаційна система озвучення україномовного тексту на основі методів NLP та машинного навчання
dc.title.alternativeInformation system for ukrainian text voiceover based on NLP and machine learning methods
dc.typeArticle

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