# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 4 Conclusions  
This study presented an end-to-end model incorporating a mixture alignment mechanism based on FastSpeech2 for Tibetan speech synthesis. Firstly, the Tibetan text underwent preprocessing, with the characters transcribed into Latin letters following the Wylie transcription rules. Then, the mixture alignment FastSpeech2 model was proposed to mitigate errors arising from the rigid alignment of Latin letters. Additionally, to bolster the model's generalization capabilities, the synthesis model underwent training using an English dataset before transferring the pre-trained model to Tibetan speech synthesis.  
Experimental results demonstrate significant improvements in the synthesized speech quality achieved by the mixed alignment FastSpeech2 model. Despite the observed oscillations during the learning of pitch and energy features, these provide directions for further optimization and improvement. Future efforts will focus on integrating features such as pitch, energy, and speech styles to enhance the naturalness and expressiveness of synthesized speech.  
Future research will explore personalized speech synthesis by integrating speaker information into models to customize generated speech based on individual characteristics  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Setting** & **CMOS** \\ \hline Mixture alignment FastSpeech2 & 0 \\ Mixture alignment FastSpeech2—energy & \(-0.05\) \\ Mixture alignment FastSpeech2—pitch & \(-0.11\) \\ Mixture alignment FastSpeech2—energy—pitch & \(-0.18\) \\ FastSpeech2 & \(-0.23\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: CMOS comparison in the ablation studies.  
and styles. Personalized speech synthesis not only tailors speech generation to speaker attributes but also enhances the user's experience by meeting specific needs.  
With advancements in artificial intelligence and deep learning technologies, large models demonstrate immense potential in speech synthesis. Models like GPT-4, T5, and BERT possess strong learning and generalization capabilities, better capturing and expressing language complexity. Future applications aim to further improve Tibetan speech synthesis quality and effectiveness by leveraging these large models. By incorporating multilingual training and cross-lingual learning, these models will utilize diverse data resources from other languages to further enhance Tibetan speech synthesis.  
Addressing technical challenges will involve exploring data augmentation techniques, model optimization strategies, and enhancing computational efficiency. These efforts will elevate Tibetan speech synthesis technical standards and broaden its applications in education, cultural dissemination, and intelligent assistants. In conclusion, large models show promising application prospects in Tibetan speech synthesis. Continuous innovation and optimization aim to contribute possibilities and opportunities for Tibetan language development and dissemination.  
Methodology, Q.Z.; Validation, Q.Z.; Writing--original draft, Q.Z.; Writing--review & editing, Q.Z. and XX.; Supervision, X.X. and Y.Z.; Project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 61976236.  
The datasets presented in this article are not readily available because they are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.