## 7 Experiment 2: Comparison of SER Methods for Amharic Cross-Lingual SER  
The aim was to compare the three models AlexNet, VGGE, and ResNet50 (Section 4) when applied to cross-lingual SER. This time, the systems are trained on data in one \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ASED** & **EMO-DB** & **RAVDESS** \\ \hline AlexNet & 80.93 & 55.74 & 82.22 \\ \hline VGGE & 86.63 & 70.49 & 83.33 \\ \hline ResNet50 & 85.82 & 73.77 & 77.78 \\ \hline
**Average** & 84.46 & 66.67 & 81.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experiment 1.2: Monolingual SER results, expressed as accuracy, for the different datasets. Training and testing sentences are varied in this experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ASED** & **EMO-DB** & **RAVDESS** & **URDU** \\ \hline AlexNet & 78.71 & 68.52 & 80.63 & 93.75 \\ \hline VGGE & 84.76 & 85.19 & 83.13 & 70.00 \\ \hline ResNet50 & 84.13 & 79.63 & 84.38 & 90.00 \\ \hline
**Average** & 82.53 & 77.78 & 82.71 & 84.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experiment 1.1: Monolingual SER results expressed as accuracy for different models and datasets (train in one language, test in the same language). Training and testing speakers are varied in this experiment. All datasets are monolingual: the languages are Amharic (ASED), German (EMO-DB), English (RAVDESS), and Urdu (URDU). language and then tested on data in another language. Firstly, the three models are trained on ASED and tested on EMO-DB, then trained on EMO-DB and tested on ASED, and so on, for different combinations. To allow this cross-training, dataset-specific emotion labels are mapped into two classes, positive and negative, using the same method as for Experiment 1. Once again, MFCC features were used for all models. The network configuration for VGGE was the same as in the preceding Experiment (Figure 1). For the other models, the standard configuration and settings were used. The results are presented in Table 7. As line 1 of the table shows, we first trained on ASED and evaluated on EMO-DB (henceforth written ASED\(\rightarrow\)EMO-DB). VGGE gave the best accuracy (66.67%), followed closely by AlexNet (65.80%) and then ResNet50 (64.06%). For EMO-DB\(\rightarrow\)ASED, VGGE was best (64.22%), also followed by AlexNet (62.39%) and then ResNet50 (58.72%). Next, for ASED\(\rightarrow\)RAVDESS, AlexNet was best (66.00%), followed by ResNet50 (61.75%) and VGGE (59.25%). For RVDESS\(\rightarrow\)ASED, AlexNet was best (65.87%), closely followed by ResNet50 (64.16%) and then VGGE (61.43%). Thirdly, we used ASED\(\rightarrow\)URDU. Here, ResNet50 was best (61.56%), followed by AlexNet (60.00%) and VGGE (59.69%). For URDU\(\rightarrow\)ASED, ResNet50 was best (61.33%), followed by VGGE (60.00%) and AlexNet (50.67%). It is interesting that for ASED\(\leftrightarrow\)EMO-DB, VGGE was best; for ASED\(\leftrightarrow\)RAVDESS, AlexNet was best; and for ASED\(\leftrightarrow\)URDU, ResNet50 was best. What is more, the figures for AlexNet on ASED\(\leftrightarrow\)RAVDESS in the two directions (66.00%, 65.87%, difference 0.13%) were very close, as were those for ResNet50 on ASED\(\leftrightarrow\)URDU (61.56%, 61.33%, difference 0.23%), while those for VGGE on ASED\(\leftrightarrow\)EMO-DB (66.67%, 64.22%, difference 2.45%) were slightly further apart. We can therefore conclude that the performance of the three models was very similar overall. This is supported by the average accuracy figures for AlexNet, VGGE, and