## 8 Experiment 3: Multilingual SER  
In the previous experiment, we trained in one language and tested in another. In this final experiment, we trained on several non-Amharic languages and then tested on Amharic. The same three models were used, AlexNet, VGGE, and ResNet50, with the same settings and training regime as in the previous experiments. Table 8 shows the results. Recall that the languages are Amharic (ASED), German (EMO-DB), English (RAVDESS), and Urdu (URDU). The first three rows for each model show the results when two datasets were used for training, EMO-DB+RAVDESS, EMO-DB+URDU, and RAVDESS+URDU. The fourth row uses all three datasets for training, i.e., EMO-DB+ RAVDESS+URDU. In all cases, testing is with ASED. The best overall performance in the table is for VGGE, training with EMO-DB+URDU (69.94%). The average figure for VGGE over all the dataset training combinations is also the best (66.44%). When RAVDESS is added to EMO-DB+URDU to make EMO-DB+RAVDESS+URDU, the performance of VGGE falls by 1.53% to 68.41%. In the results presented in Table 9, the upper right-hand column shows the average accuracy, and the lower right-hand column the average F1-score. In this case, we see that the highest figures over all three models are for all three datasets (67.12% and 59.79%, respectively). However, the most interesting result here is that the best accuracy figure in Table 8 (EMO-DB+URDU\(\rightarrow\)ASED, VGGE, 69.94%) is higher than the best accuracy figure in Table 7 with ASED as the target, (RAVDESS\(\rightarrow\)ASED, AlexNet, 65.87%) by 4.07%. In other words, training on German and Urdu gives a better result for Amharic than training on English alone. Moreover, the best overall average accuracy figure in Table 8 (VGGE, 66.44%) is higher than the best overall average accuracy figure in Table 7 (ResNet50, 61.93%) by 4.51%. Once again, the results in Table 8 show that the average F1-score performance for VGGE (62.78%) is higher than that for AlexNet (55.81%, 6.97% lower) and ResNet50 (51.65%, 11.13% lower). Furthermore, the best overall average F1-score figure in Table 8 (VGGE, 62.78%) is higher than the best overall average F1-score figure in Table 7 (VGGE, 55.99%) by 6.79%. These results suggest that by using several non-Amharic datasets for training, we can obtain a better result, by several percentage points, than when using one non-Amharic dataset for training, when testing on Amharic throughout. Compared with the existing studies discussed in Section 2, there are only three that present multilingual experiments. [PERSON] et al. (2013) report that training on three datasets, EMO-DB, DES, and ENT, and testing on EMO-DB gave the best result, better than their \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training** & **Testing** & **Accuracy** & **F1-Score** \\ \hline AlexNet & EMO-DB + RAVDESS & ASED & 69.06 & 61.28 \\  & EMO-DB + URDU & ASED & 57.23 & 48.38 \\  & RAVDESS + URDU & ASED & 62.46 & 51.27 \\  & EMO-DB + RAVDESS + URDU & ASED & 69.77 & 62.30 \\ \hline
**Average** & & & 64.63\% & 55.81\% \\ \hline VGGE & EMO-DB + RAVDESS & ASED & 60.50 & 61.12 \\  & EMO-DB + URDU & ASED & 69.94 & 65.26 \\  & RAVDESS + URDU & ASED & 66.89 & 64.56 \\  & EMO-DB + RAVDESS + URDU & ASED & 68.41 & 60.17 \\ \hline
**Average** & & & 66.44\% & 62.78\% \\ \hline ResNet50 & EMO-DB + RAVDESS & ASED & 61.33 & 43.52 \\  & EMO-DB + URDU & ASED & 46.24 & 44.57 \\  & RAVDESS + URDU & ASED & 64.51 & 56.17 \\  & EMO-DB + RAVDESS + URDU & ASED & 63.18 & 62.32 \\ \hline
**Average** & & & 58.82\% & 51.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experiment 3: Multilingual SER results (train in two or three non-Amharic languages, test in Amharic). The languages are Amharic (ASED), German (EMO-DB), English (RAVDESS), and Urdu (URDU).