fb536c28-c1c7-49d6-b6cb-b4a09115985f20210310050455824naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037430202043020201410.46300/9109.2020.14http://www.naun.org/cms.action?id=23206ElenaOdarushchenkoPoltava State Agrarian Academy, Poltava, UKRAINEValentinaButenkoNational Aerospace University “KhAI”, Computer Systems and Networks Department, Kharkiv, UKRAINEViktorSmolyarPoltava State Agrarian Academy, Poltava, UKRAINEVyacheslavKharchenkoNational Aerospace University “KhAI”, Computer Systems and Networks Department, Kharkiv, UKRAINEIntroduction of augmented reality into the E-learning systems brings a new era in personal remote education. While E-learning brings a lot of benefits for both instructor and student it also has various difficulties, such as absence of vital personal interaction that can influence on student motivation and inspiration. To decrease this gap we have started the development of an Interactive Adaptable Learning Interface (IALI) that with the help of speech synthesizers, emotions and voice recognition systems tries to adapt the ongoing E-learning session to the student behavior, simulating the way instructor adapt the lesson to the student reactions. As the IALI is initially planned for Ukrainian language students, we have made a deep analysis of nowadays applicable solutions for Ukrainian speech synthesis and recognition. In this paper we present the conducted analysis and application of the most widely applied speech synthesizers, voice and emotions recognition systems that were used to develop first version of IALI.1120202011202020115120https://www.naun.org/main/NAUN/educationinformation/2020/a282008-014(2020).pdf10.46300/9109.2020.14.14http://www.naun.org/main/NAUN/educationinformation/2020/a282008-014(2020).pdfArkorful, V., Abaidoo, N.: The role of e-learning, the advantages and disadvantages of its adoption in Higher Education. International Journal of Education and Re-search 2(12), 397-410 (2014). Holmes, B., Gardner, J.: E-Learning: concepts and practice. 1st edn. SAGE Publications Ltd., London (2006). 10.1007/s00779-013-0747-yRadu, I.: Augmented reality in education: a meta-review and cross-media analysis. Personal and Ubiquitous Computing 18(6), 1533-1543 (2014). Donally, J.: Learning transported: augmented, virtual and mixed reality for all classrooms. 1st edn. ISTE, London (2018) EECS20N: Signals and systems, parametric speech synthesis, https://ptolemy.berkeley.edu/eecs20/speech/voder.html, last accessed 2019/10/12.Taylor, P.: Text to speech synthesis. 1st edn. Cambridge University Press, Cambridge (2009). Dutoit, T.: An introduction to text-to-speech synthe-sis. 1st edn. Springer, Dordrecht (1997). BIBLRPROG Windows, UkrVox page, https://biblprog.org.ua/ua/ukrvox, last accessed 2019/03/20. Trosti, Anatol’ homepage, http://www.trosti.com.ua/ua/anatol.html, last accessed 2019/03/20. CyberMova homepage, http://cybermova.com, last accessed 2019/03/20. pyttsx3 2.7 project, https://pypi.org/project/pyttsx3/2.7, last accessed 2019/04/10. pypiwin32 219 project, https://pypi.org/project/pypiwin32/219, last accessed 2019/04/10.10.1109/icassp.2016.7472618Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabu-lary speech recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Proceeding, pp. 1-8, IEEE, Shanghai, China (2016).10.1109/icassp.2018.8461972Toshniwal, S., Weiss, R., Sainath, T., et al.: Multilingual speech recognition with a single end-to-end model. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Proceeding, pp. 1-8, IEEE, Shanghai, China (2016).10.1109/jbhi.2017.2688239Katsigiannis, S., Ramzan, N.: DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE Journal of Biomedical and Health Informatics 22(1), 98-107 (2017).Cambria, E., Hussain, A.: Sentic computing: a commonsense-based framework for concept-level sentiment analysis. 1st edn. Springer, Heidelberg (2015).CMUSphinx Homepage, https://cmusphinx.github.io, last accessed 2019/10/12.Vokaturi Homepage, https://vokaturi.com, last accessed 2019/10/12.