# Makedonka: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language
## 6 Conclusions and Future Work  
This paper presents a successful effort to train a human-like TTS model for the Macedonian language. After many attempts to implement an efficient and humanoid TTS system for the Macedonian language in the last 30 years, we are the first who built it and published the model that is available to use as a module in any kind of software that needs its service.  
The methodology presented in the paper relies on a previously confirmed Deep learning-based methodology--Deep Voice 3. We built software for new records management and created approximately 20 h-long training corpus from scratch in order to achieve a high-quality model. The dataset has been preprocessed by following the example of the Ljspeech dataset [44], which is considered to be the golden standard for training English language TTS systems. The deep neural network has been adjusted according to the Macedonian language needs.  
Intelligible speech has been synthesized after 56 K steps of training, and acceptable quality has been achieved, even after 100 K steps of training. However, the model has been improved in the later steps, and the robotic-like components in the synthesized speech have been almost removed after 200 K steps of training. The quality of the generated audio files has been assessed while using the MOS metric, which is commonly used to assess the TTS systems.  
Many intelligent systems may benefit from such a TTS system, as are the recommendation systems developed for the social media networks [53; 54; 55], by establishing an interaction with the users and, thus, improving their experience by achieving human-like communication.  
In future work, we will extend the dataset with new sentences that cover as many different fields as possible, including many new words with different pronunciation complexity. We will also work on improving the pronunciation of words with irregular accents in the Macedonian language. The word stress in the Macedonian language is antepenultimate and dynamic, which means that it falls on the third from last syllable in words with three or more syllables, and on the first or only syllable in other words. However, this rule is sometimes disregarded when the word is among the more recent ones in the language or it is from a foreign language.  
The training dataset, the synthesized samples at different checkpoints, the source code, and the trained model are publicly available for further improvement on GitHub [[https://github.com/f-data/TTS-Makedonka](https://github.com/f-data/TTS-Makedonka)]([https://github.com/f-data/TTS-Makedonka](https://github.com/f-data/TTS-Makedonka)).  
**Author Contributions:** Conceptualization, K.M., A.K.R., D.T., T.E. and M.S.; Formal analysis, T.E.; Funding acquisition, T.E.; Investigation, D.T. and M.S.; Methodology, K.M., T.E. and M.S.; Project administration, A.K.R.; Resources, M.S.; Software, K.M.; Supervision, M.S.; Validation, K.M. and D.T.; Visualization, K.M.; Writing--original draft, K.M., T.E. and M.S.; Writing--review & editing, K.M., A.K.R., T.E. and M.S. All authors have equally contributed to this work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by project from the Slovenian Research Agency [research core grant number P2-0098], and was partially funded by Faculty of Computer Science and Engineering.  
**Acknowledgments:** The work was inspired by Erasmus+ KA203 FAST: Fostering Accessible Study Technologies--Accessible Learning Management System in Humanities and Social Sciences (project reference: 2018-1-MK01-KA203-047104).  
**Conflicts of Interest:** The authors declare no conflict of interest.