## 5 Results and Discussion  
We evaluated the performance of the TTS model using subjective and objective metrics. For a subjective evaluation of naturalness, we randomly selected 50 real test audio samples along with their text transcripts for each language and measured the Mean Opinion Score. The Mean Opinion Score (MOS) is a subjective rating system in which human evaluators assess the perceived quality of synthesized speech on a scale from 1 to 5. It is one of the most widely used evaluation methods for TTS systems. Here, for each text transcript, we synthesize speech using six different models, including the baseline VITS and the extended BERTIVITS model with the new posterior encoder. For each language, we enlist 10 professionals to rate each of the five types of speech (four synthesized speech samples and one real speech sample). Naturalness is rated on a scale from 1 to 5, with increments of 1 point. Here, each evaluator is unaware of which model produced which speech sample. The results are as shown in Tables 1 and 2. To objectively evaluate the distortion and prosodic differences between real and synthesized speech, we calculate the Mel-Cepstral Distortion (MCD; dB) for comparison. Mel-cepstral distortion is an objective metric used to assess synthetic voices. It quantifies the difference between two sequences of Mel (Melody) cepstra. The results are presented in Tables 3 and 4. During the training process, we also recorded the Root Mean Square Error (RMSE) and training speed (batch size = 64) of different models on the same dataset. The RMSE indicates the absolute difference between the generated speech and the original speech, while the training speed roughly reflects the difficulty of training different models. When training different models, we kept the hyperparameters consistent and set the gradients of the pre-trained model to be non-updatable. The comparison of the RMSE and training speed is shown in Tables 5 and 6. \begin{table}
\begin{tabular}{c c} \hline
**Model** & **MOS (\(\uparrow\))** \\ \hline Ground Truth & 4.72 \(\pm\) 0.06 \\ VITS-Baseline & 4.00 \(\pm\) 0.08 \\ VITS-XPB & 4.14 \(\pm\) 0.07 \\ VITS-Pseudo Phoneme (wav2 vec2.0) & 3.99 \(\pm\) 0.10 \\ VITS-Pseudo Phoneme (HuBERT) & 4.11 \(\pm\) 0.07 \\ BERTIVITS (our) & **4.16 \(\pm\) 0.06** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the MOS scores for all models on the LJSpeech dataset. \begin{table}
\begin{tabular}{c c} \hline
**Model** & **MOS (\(\downarrow\))** \\ \hline Ground Truth & 0.00 \\ VITS-Baseline & 7.04 \\ VITS-XPB & 6.63 \\ VITS-Pseudo Phoneme (wav2 vec2.0) & 6.77 \\ VITS-Pseudo Phoneme (HuBERT) & 6.65 \\ BERTIVITS (our) & **6.52** \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the MCD for all models on the LJSpeech dataset. \begin{table}
\begin{tabular}{c c} \hline
**Model** & **MOS (\(\uparrow\))** \\ \hline Ground Truth & 4.72 \(\pm\) 0.06 \\ VITS-Baseline & 4.00 \(\pm\) 0.08 \\ VITS-XPB & 4.14 \(\pm\) 0.07 \\ VITS-Pseudo Phoneme (wav2 vec2.0) & 3.99 \(\pm\) 0.10 \\ VITS-Pseudo Phoneme (HuBERT) & 4.11 \(\pm\) 0.07 \\ BERTIVITS (our) & **4.16 \(\pm\) 0.06** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the MOS scores for all models on the Database dataset. \begin{table}
\begin{tabular}{c c} \hline
**Model** & **MOS (\(\uparrow\))** \\ \hline Ground Truth & 4.66 \(\pm\) 0.04 \\ VITS-Baseline & 3.53 \(\pm\) 0.07 \\ VITS-XPB & 3.87 \(\pm\) 0.08 \\ VITS-Pseudo Phoneme (wav2 vec2.0) & 3.66 \(\pm\) 0.06 \\ VITS-Pseudo Phoneme (HuBERT) & 3.78 \(\pm\) 0.07 \\ BERTIVITS (our) & **3.89 \(\pm\) 0.07** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the MOS scores for all models on the Database dataset. Tables 1 and 2 both provide MOS score results on two datasets. It can be observed that our model contributes to improving the performance of the VITS model on this evaluation metric. Our model achieved significant improvements of 0.16 and 0.36 on the two datasets respectively. This clearly demonstrates the effectiveness of our model.