# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 3 Experiments and Results
### Evaluation Indicators and Experimental Results
#### 3.5.4 Objective Evaluation Results  
The evaluation results for the objective metrics are presented in the following tables. Table 5 shows the \(RMSE\) results for different models. A smaller \(RMSE\) value indicates better performance of the model in terms of synthesizing speech that closely matches the original speech. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & \(p\)**-adj** \\ \hline FastSpeech2 & 0.0065 \\ FastSpeech2+ pre-trained model & 0.0065 \\ Mixture alignment FastSpeech2 & 0.3734 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Post-hoc test results. As indicated in Tables 5 and 6, the \(RMSE\) values for the mixture alignment FastSpeech2 model decreased, reflecting improved accuracy in speech synthesis. The utilization of the pre-trained model on the English dataset resulted in a smaller \(RMSE\) compared to models without pre-training, enhancing the quality of synthesized speech. Additionally, the \(RTF\) values demonstrate that the mixture alignment FastSpeech2+ pre-trained model processes speech more efficiently, achieving the lowest \(RTF\) value of 0.186. This indicates that this model is not only more accurate but also more efficient in real-time speech synthesis applications. Training Time and Inference Speed  
Table 7 compares the training times and inference speeds of different models. This comparison highlights the practical aspects of model deployment and efficiency. When comparing the Transformer TTS and mixture alignment FastSpeech2 models, the latter demonstrates significant advantages in both training time and inference speed, as detailed in Table 7. Mixture alignment FastSpeech2 requires only 45 h for training, markedly less than the 122 h needed by Transformer TTS. This reduction is largely due to the autoregressive nature of Transformer TTS, which necessitates a longer training period to capture detailed speech data, whereas mixture alignment FastSpeech2 employs a more efficient alignment mechanism. In terms of inference speed, mixture alignment FastSpeech2 outperforms Transformer TTS with a real-time factor of 0.193 compared to 0.984 for Transformer TTS, highlighting its superior efficiency. Although mixture alignment FastSpeech2 has a slightly longer training time compared to FastSpeech2 (37 h), this is due to the additional computations and adjustments required by its mixed alignment mechanism. Nonetheless, this added complexity results in a substantial increase in inference speed, making mixture alignment FastSpeech2 a more practical and efficient choice for real-world applications. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & \(RTF\) \\ \hline FastSpeech2 & 0.216 \\ FastSpeech2+ pre-trained model & 0.204 \\ Mixture alignment FastSpeech2 & 0.193 \\ Mixture alignment FastSpeech2+ pre-trained model & 0.186 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(RTF\) evaluation results for different models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & \(RMSE\) \\ \hline FastSpeech2 & 0.38 \\ FastSpeech2+ pre-trained model & 0.28 \\ Mixture alignment FastSpeech2 & 0.32 \\ Mixture alignment FastSpeech2+ pre-trained model & 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(RMSE\) evaluation results for different models. Both subjective and objective evaluation results indicate that using a pre-trained model has led to higher quality and naturalness in speech synthesis. We speculate that the reasons for this improvement may be as follows: firstly, the pre-trained model adeptly assimilates intricate feature representations through its training on the LJSpeech dataset, seamlessly transferring these acquired insights to the realm of Tibetan speech synthesis. Secondly, the pre-trained model demonstrates a knack for absorbing pertinent audio and acoustic nuances inherent in the speech, thereby enhancing its capacity to faithfully capture the underlying structure and essence of the spoken content.