# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 5 Conclusions  
This paper mainly studied the method of Mongolian speech synthesis. First of all, we assessed various parameters in order to solve the problem of Mongolian characters having the same shape and different pronunciations in different environments, and the problem that Mongolian letters are written differently at the beginning, middle, and end of words, which causes the model to be unable to accurately recognize. Therefore, in the front-end part of Mongolian speech synthesis, a Bang-based Mongolian phoneme pre-training model was constructed, which reduced errors, such as missing words and skipped words in generated speech, and improved the quality of Mongolian speech synthesis.  
Because existing Mongolian acoustic models use a large number of convolutional neural network structures, resulting in low training efficiency, we proposed the Mongolian speech synthesis model Ghost-ILPCnet based on Ghost and ILPCnet. This model improves the current situation of low training efficiency of the Mongolian speech synthesis model, poor real-time speech generation rate, and unnatural speech synthesis effect. Through experimental verification, the naturalness of the speech synthesized by this model was closer to the original audio. However, this method still has room for improvement. Due to the use of techniques, such as sequence distillation and fine-tuning in the pre-training process of phonemes, the model training time was extended, which affected the speed of speech synthesis to a certain extent. Therefore, further improvements need to be made in future work to improve the performance of speech synthesis. At the same time, the current Mongolian speech synthesis model has a single timbre and lacks emotion. Therefore, on the basis of continuously improving the quality of the corpus, how to improve the emotional expression of Mongolian speech synthesis is also the focus of the next step of research.  
Conceptualization, Q.-D.-E.-J.R. and L.L.; methodology, Q.-D.-E.-J.R., L.W. and L.L.; software, L.W.; validation, L.W.; formal analysis, W.Z.; investigation, L.W.; resources, Q.-D.-E.-J.R.; data curation, L.W.; writing--original draft preparation, Q.-D.-E.-J.R., L.W. and W.Z.; writing--review and editing, W.Z. and L.W.; visualization, L.W.; supervision, Q.-D.-E.-J.R. and W.Z.; project administration, Q.-D.-E.-J.R. and L.W.; funding acquisition, Q.-D.-E.-J.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62066035, 62206138), Inner Mongolia Natural Science Foundation (2022 MS06013, 20222 LHMS06004), Inner Mongolia Science and Technology Program Project (2021 GG0140, 2020 GG0104), Support Program for Young Scientific and Technological Talents in Inner Mongolia Colleges and Universities (NJYT23059), universities directly under the autonomous region funded by the Fundamental Research Fund Project (JY20220122, JY20220089, RZ2300001739, RZ2300001743, JY20220186), and basic scientific research business expenses of universities directly in the Inner Mongolia Autonomous Region (ZTY2023021, JY20220419).  
Not applicable.  
The data presented in this study are openly available in [China Scientific Data] at [[[https://doi.org/10.11922/sciencedb.j00001.00345](https://doi.org/10.11922/sciencedb.j00001.00345)]([https://doi.org/10.11922/sciencedb.j00001.00345](https://doi.org/10.11922/sciencedb.j00001.00345))] (accessed on 6 January 2024), reference number [1-7].  
The authors declare no conflicts of interest.