# An Emotion Speech Synthesis Method Based on VITS
## 4 Experiment and Result
### Objective Evaluation  
Firstly, an accuracy test was conducted, which was mainly used to evaluate whether emotion embedding had an impact on the accuracy of speech synthesis itself. Generally speaking, compared with pure speech synthesis, the naturalness of emotion speech synthesis will decrease due to the addition of emotion factors. Therefore, what we want to evaluate with word error rate (WER) is whether the inclusion of emotional embedment will lead to the decrease of naturalness and accuracy of speech [27]. The random audio within the training dataset was used as the reference audio, with 100 synthesized sentences of audio, and the WER was tested through a speech recognition (ASR) code. The speech recognition code here is provided by Baidu AI, and the accuracy rate has reached more than 98.4%. The results are shown in Table 1.  
As can be seen from the table, after the emotional network is added to the acoustic model VITS, WER increases, but the amplitude of increase is acceptable. In our test, WER did not increase much after the addition of the emotion module to the Biaobei dataset, but the increase in the Pm dataset was greater than that of Biaobei dataset. This may be because Pm is a homemade dataset. When the amount of data is small, the influence of adding an emotion module on the accuracy of synthesis will be amplified. It is worth mentioning that the LST-A structure is not effective in this accuracy experiment, and the error rate is very high. We believe that the reason is that LST-A's TransformerTTS is not robust enough, and its performance is not as stable as that of VITS in the case of small data. This is also an advantage of our use of the VITS model. Next, we did an emotional classification test. First of all, we used the audio in the test dataset Pm as the reference audio for corresponding emotion synthesis, and then used the pre-trained emotion recognition model to detect whether our emotion speech synthesis based on reference audio can restore the emotion. The accuracy of the speech emotion recognition model used here is around 80% [31]. In emotion recognition, _WA_ (Weighted Accuracy) and _UA_ (Unweighted Accuracy) are generally used to measure the accuracy rate. The formula is as follows:  
\[WA=\frac{\sum_{i=1}^{N}TP_{i}}{\sum_{i=1}^{N}TP_{i}+FP_{i}}\times 100\% \tag{10}\]  
where \(N\) represents the total number of categories. \(TP\) represents the sample with a positive true value and a positive predicted value, and \(FP\) represents the sample with a negative true value and a positive predicted value. \[acc_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}}\times 100\% \tag{11}\]  
\[UA=\frac{1}{N}\sum_{i=1}^{N}acc_{i}\times 100\%\]  
where, \(acc\) represents the accuracy rate of each category, and \(N\) represents the total number of categories. A total of 100 sentences of audio were selected as reference audio in the dataset. We controlled the same text information. At the same time, in order to minimize the impact brought by the accuracy of the emotion recognition model itself, we took the result of the reference audio used for testing after passing the emotion recognition model as the correct \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Biaobei**} & \multicolumn{3}{c}{**Pm**} \\ \hline  & VITS & Emo-VITS & LST-A & VITS & Emo-VITS & LST-A \\ \hline WER (\%) & 8.1\% & 9.2\% & 14\% & 15.9\% & 19.6\% & 25.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Objective evaluation of Biaobei and Pm datasets. emotion. The total accuracy of emotion synthesis and the accuracy of emotion transfer are shown in Table 2.  
It can be seen that under the classification test of five emotions, the model in this paper can finally achieve 61% accuracy of emotion synthesis. That is, after more than half of the audio is used as the reference audio, the output audio still retains the original emotional features and is classified into the same category in the emotional classification.