# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### Pre-Training Method  
Currently, the amount of available Tibetan language corpus is relatively limited, and relying solely on parallel Tibetan corpora for system training yields suboptimal results. Inspired by transfer learning, this paper proposes a pre-training method to address this issue.  
In the context of deep neural network-based transfer learning methods, fine-tuning is one of the simplest and most convenient approaches. Fine-tuning involves pre-training a model on one task and then adapting it to a related task by training it on new data specific to the target domain. This method significantly reduces training time and leverages features already learned by the pre-trained model, thereby enhancing performance on the new task. While fine-tuning does increase some training costs, its significant advantage lies in accelerating the training process, reducing the amount of data required and improving the final model's performance. These benefits typically outweigh the additional costs. For resource-scar languages like Tibetan, this pre-training method is particularly valuable. Compared to English and Chinese, Tibetan has very limited linguistic resources. By pre-training a model on a more resource-rich language and then fine-tuning it with Tibetan data, we can effectively utilize the extensive resources available for other languages, thereby improving the performance of Tibetan speech synthesis systems. This approach not only compensates for the lack of Tibetan data but also ensures the system performs well even with limited data.  
Specifically, we first train an English speech synthesis system using \"text-to-audio\" data from a large English dataset. For this purpose, we used the publicly available LJSpeech dataset [24], which contains high-quality text-to-audio pairs that have been carefully selected and processed to ensure suitability for pre-training. In the initial training phase, we train the FastSpeech2 and mixture alignment FastSpeech2 models using the LJSpeech dataset. This process includes initializing the model with predefined parameters and conducting several training iterations to gradually learn the mapping between English text and corresponding speech. During training, we use the loss functions mean absolute error (MAE) and mean squared error (MSE) to optimize the model parameters. To prevent overfitting, we periodically evaluate the model's performance using a validation set.  
After completing the initial training with the English dataset, we proceed with transfer learning. In this phase, we keep certain encoder and decoder weights fixed, particularly those layers that have effectively captured speech features. We then retrain the model using \"text-to-audio\" data in the target language to adapt the model to the new language characteristics. By adjusting the weights of the unfrozen layers, the model gradually learns the features of the target language.  
This transfer learning method not only accelerates the decoder's learning of speech feature information but also swiftly establishes correspondence between phonetic and textual units in the target language, akin to those in the source language. Furthermore, it effectively leverages encoder information to expedite the establishment of the language model and the extraction of text information.