## 5 Results and Discussion
In terms of Mel-Cepstral Distortion (MCD), as shown in Tables 3 and 4, our model contributes to improving the objective quality of speech synthesized by the VITS model. Mel-Cepstral coefficients decreased by 0.52 and 0.49 on the two datasets respectively. Here, we observe that the magnitude of the MCD reduction and MOS improvement are not always directly proportional, which is consistent with the findings in [38]. Additionally, as indicated in Tables 5 and 6, our model did not significantly increase training time, although the training time per epoch for our model is higher than that of VITS-XPB. However, XPhoneBERT has not been pre-trained on a large corpus of Chinese data; it has primarily focused on English and Vietnamese. Given significant phonetic differences among languages, we have not fine-tuned the original XPhoneBERT for Chinese. Thus, our current work has not fully explored the potential of XPhoneBERT in the Chinese domain. In the future, we will consider adopting the training methodology and data preprocessing techniques of XPhoneBERT to create a large-scale Chinese phoneme corpus and conduct pre-training on it. We selected the same text and used VITS, VITS-XPB, VITS-Pseudo Phoneme (HuBERT), and BERTIVITS to plot spectrograms, as shown in Figure 4. The comparison is shown in the figure, indicating that our model can improve the spectral details of the output of the VITS model. Observing the spectrograms, our model performs significantly better than VITS-Baseline in restoring frequencies above 4000 Hz and does well in restoring real audio in the 2000-4000 Hz frequency range. That is, it can effectively restore human voice while capturing more high-frequency details. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **RMSE** & **Train Speed (min/Epoch)** \\ \hline Ground Truth & 0.00 & 0 \\ VITS-Baseline & 21.77 & 1.27 \\ VITS-XPB & 19.89 & 2.94 \\ VITS-Pseudo Phoneme (wav2 vec2.0) & 20.16 & 5.11 \\ VITS-Pseudo Phoneme (HuBERT) & 19.92 & 5.37 \\ BERTIVITS (our) & **18.82** & **3.73** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the RMCE and train speed for all models on the Database dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **RMSE** & **Train Speed (min/Epoch)** \\ \hline Ground Truth & 0.00 & 0 \\ VITS-Baseline & 18.26 & 1.04 \\ VITS-XPB & 15.37 & 2.85 \\ VITS-Pseudo Phoneme (wav2 vec2.0) & 16.44 & 4.91 \\ VITS-Pseudo Phoneme (HuBERT) & 15.66 & 5.14 \\ BERTIVITS (our) & **14.71** & **3.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the RMCE and train speed for all models on the LJSpeech dataset. In summary, our model significantly improves the performance of the VITS model in various aspects through multiple enhancements, including increased subjective evaluation scores, improved objective evaluation metrics, and optimization of spectrogram details. Our method achieves significant results on single-speaker datasets, but its effectiveness is not pronounced in multi-speaker scenarios. The differences in voices of different individuals vary significantly, including variations in fundamental frequency, energy, and consonants, among many other aspects. The pre-trained model HuBERT was originally designed for tasks such as speech recognition, which may not fully align with the requirements of tasks like speech synthesis and voice cloning. Its effectiveness in our task stems from the fact that in single-speaker datasets, where only one person's voice needs to be reconstructed, we only need to consider the characteristics of that individual's voice. If considering multi-speaker scenarios, adjustments to the architecture of the pre-trained model are necessary. This involves placing greater emphasis on extracting features specific to different speakers and embedding speaker identity information. Alternatively, specialized optimization of corresponding modules in the VITS model, such as pre-training the vocoder and fine-tuning other modules, may be required to better accomplish the tasks at hand.