# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 5 Evaluation
### Automatic Recognition
#### 5.2.6. Speaker Identification Results  
Table 12 provides a summary of the results of speaker identification using the original, converted, merged, and crossed database, which includes the mean accuracy for 10 runs, higher and lower accuracy, and the standard deviation. The results are presented for males, females, and males and females together. For further understanding, Figure 13 presents the overall average accuracy. According to the table and the figure, the identification accuracy of female speakers was 94.97% greater than that of male speakers, which was 91.85%, and greater than male and female speakers combined, which was 91.44%. Compared with the converted data, we noticed that the accuracy of identifying male and female speakers is comparable, with a small increase for males. The accuracy of speaker identification reduced somewhat (0.5% for males, 2.66% for females, and 3.07% for all speakers) when the original data were combined with the converted data. In the case of crossed data, males have the highest accuracy, as depicted in the image.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Original**} & \multicolumn{4}{c}{**Converted**} & \multicolumn{4}{c}{**Merged**} \\ \hline  & & **Female** & **Male** & **MF** & **Female** & **Male** & **MF** & **Female** & **Male** & **MF** \\ \hline \multirow{2}{*}{**Arousal**} & **Acc** & 92.27 & 81.09 & 82.73 & 95.47 & 81.09 & 82.73 & 95.39 & 87.89 & 87.73 \\ \cline{2-11}  & **STD** & 0.02 & 0.07 & 0.04 & 0.02 & 0.07 & 0.04 & 0.01 & 0.03 & 0.03 \\ \hline \multirow{2}{*}{**Valence**} & **Acc** & 85.15 & 87.56 & 88.08 & 72.34 & 81.72 & 80.63 & 87.34 & 84.06 & 88.13 \\ \cline{2-11}  & **STD** & 0.04 & 0.04 & 0.02 & 0.06 & 0.06 & 0.04 & 0.05 & 0.03 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 11. Average accuracy of 10 runs for arousal–valence emotion recognition.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Female**} & \multicolumn{4}{c}{**Male**} & \multicolumn{4}{c}{**MF**} \\ \hline  & **Ori** & **Conv** & **Mer** & **Cro** & **Ori** & **Conv** & **Mer** & **Cro** & **Ori** & **Conv** & **Mer** & **Cro** \\ \hline
**Average** & 94.97 & 85.62 & 92.31 & 50.19 & 91.85 & 86.88 & 91.34 & 66.28 & 91.43 & 83.44 & 88.36 & 56.45 \\ \hline
**Std** & 3.45 & 5.30 & 1.52 & 4.13 & 3.10 & 4.11 & 2.76 & 5.02 & 4.73 & 4.60 & 3.64 & 5.17 \\ \hline
**Best** & 98.86 & 93.75 & 94.54 & 56.11 & 95.83 & 95.31 & 96.12 & 76.03 & 96.78 & 88.28 & 93.62 & 64.94 \\ \hline
**Worst** & 87.43 & 76.56 & 89.50 & 44.83 & 86.31 & 81.25 & 87.93 & 59.94 & 83.04 & 75.78 & 83.19 & 49.53 \\ \hline \hline \end{tabular}
\end{table}
Table 12. Summary of results of 10 runs of speaker recognition (Ori—original, Conv—converted, Mer—merged, Cro—crossed).  
Comparing the values of the standard deviation and the difference between the highest and lowest accuracy, the value of the standard deviation for females in both the original data and the converted data is higher than the value for males, but in the case of the merged data it is the lowest.  
In the summary of the evaluation results in general, whether using the spectrum and prosody conversion or automatic recognition, we found that the results of males are, in general, better than the results of females, with a few notable exceptions. This finding is consistent with the outcomes of our previous study in emotion recognition regarding the whole of the database, whether such investigations were carried out with the human perceptual test or with the CRNN classifier [44, 45]. Regarding the used pre-trained model, the overall accuracy of emotion recognition utilizing translated data ranged from 66.17% to 77.33%, which we consider to be an acceptable level of accuracy when utilizing the pre-trained model.