# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 5 Evaluation
### Spectrum and Prosody Conversion  
We used MCD, to evaluate the performance of spectrum conversion. MCD compares the converted mel-frequency cepstra to the target mel-frequency cepstra and measures the amount of distortion. In this work, 24-dimensional mel-cepstral coefficients (MCEPs) are extracted from each frame; MCD is defined as [29,42]. \[\text{MCD}=(10/ln10)\sqrt{2\ \sum_{i=1}^{24}\bigl{(}mct_{i}^{t}-\ mc_{i}^{c} \bigr{)}^{2}}\]  
where the terms \(mct_{i}^{t}\) and \(mct_{i}^{c}\) stand for the target and converted mel-frequency cepstra, respectively. We used root-mean-square error (RMSE) [43], which is a measure for prosody conversion performance and is defined as  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{24}\bigl{(}\bigl{(}F0_{i}^{t}\bigr{)} -\bigl{(}F0_{i}^{c}\bigr{)}\bigr{)}^{2}}\]  
where the terms \(F0_{i}^{t}\) and \(F0_{i}^{c}\) stand for the target and converted \(F0\) features, respectively. If the MCD and F0-RMSE values are low, this indicates that the distortion or prediction error is reduced as well. The MCD and F0-RMSE average results of converting one emotion to another for different combinations are reported in Table 5 for the female set, male set, and male and female set (MF). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MCD (dB)**} & \multicolumn{3}{c}{**F0\_RMSE (Hz)**} \\ \hline  & **Females** & **Males** & **MF** & **Female** & **Male** & **MF** \\ \hline  
verall mean** & 6.51 & 6.52 & 6.51 & 80.19 & 49.16 & 56.49 \\ \hline \hline \end{tabular} N2A--neutral to anger, etc.  
\end{table} 
Table 5: The average MCD and F0-RMSE results (low value is better). (MF—Females + Males; overall mean: the average of conversion from source to all other emotion). Table 5's left side displays the MCD results, while the right side displays the F0-RMSE results. For more clarity, Figures 5 and 6 show MCD and F0_RMSE for the conversion from one emotion to the other targeted emotions for females, males, and MF. Table 5 and Figures 5 and 6 show that, with a simple exception, males have the lowest values of MCD and F0 RMSE. This conclusion is consistent with our previous study in emotion recognition regarding evaluating the original database, human perceptual test results [44], convolutional recurrent neural network, and residual neural network [45], which indicated that males were evaluated more favorably than females. This result is compatible with our new findings. Figures 7 and 8 show the overall mean of MCD and F0_RMSE for each emotion. As shown in Figure 7, the lowest value of the MCD was noticed when converting from the emotion of neutral to the other different emotions and then when converting from the emotion of sadness to the other emotions. On the other hand, the highest value of MCD was observed while converting from anger to other emotions, followed by a minor difference when converting from happy to other emotions. As in the MCD result, the value of F0_RMSE is lowest when converting from neutral to other different emotions for males only or females only and when converting from sadness to other emotions for MF. However, the highest value of F0_RMSE was observed during the conversion from anger to other different emotions for females only and MF, and when converting from happiness to other emotions for males. The best and worst emotion conversion based on MCD and F0 RMSE values regarding gender are presented in Table 6. The latter is based on the findings presented in Table 5 and Figure 5, as well as Figure 6, and compares emotions that have converted from better (which got the lowest value) to worse. Neutral, sadness, happiness, and anger is the order from the best to the worst for females, males, and MF. For F0_RMSE, the best and worst are not in the same style as in MCD, as shown in Table 6, where neutral is the best for males  
Figure 5: Average MCD value for the source-target emotion conversions. Figure 6: Average F0_RMSE value for the source-target emotion conversions. only or females only and the worst is anger for females and happiness for males.