## 9 Conclusions  
In this paper, we first proposed a variant of the well-known VGG model, which we call VGGE, and then applied AlexNet, VGGE, and ResNet50 to the task of speech emotion recognition, focusing on the Amharic language. This was made possible by the existence of the publicly available Amharic Speech Emotion Dataset (ASED), which we created in our previous work [10]. In Experiment 1, we trained the three models on four datasets: ASED (Amharic), RAVDESS (English), EMO-DB (German), and URDU (Urdu). In each case, a model was trained on one dataset and then tested on that same dataset. Speaker-independent and sentence-independent training variants were tried. The results suggested that Amharic and English monolingual SER are almost equally difficult on the datasets we used for these languages, while German is harder, and Urdu is easier.  
In Experiment 2, we trained on SER data in one language and tested on data in another language, for various language pairs. When ASED was the target, the best dataset to train on was RAVDESS for AlexNet and ResNet50, and EMO-DB for VGGE. This could indicate that, in terms of SER, Amharic is more similar to English and German than it is to Urdu.  
In Experiment 3, we combined datasets for two or three different non-Amharic languages for training and used the Amharic dataset for testing. The best result in Experiment 3 (EMO-DB+URDU\(\rightarrow\)ASED, VGGE, 69.94%) was 4.07% higher than the best result in Experiment 2 (RAVDESS\(\rightarrow\)ASED, AlexNet, 65.87%). In addition, the best overall average figure in Experiment 3 (VGGE, 66.44%) was 4.51% higher than the best overall average figure in Experiment 2 (ResNet50, 61.93%). These findings suggest that if several non-Amharic datasets are used for SER training, the results can be better than if one non-Amharic dataset is used, when testing on Amharic throughout. Overall, the experiments demonstrate how cross-lingual and multilingual approaches can be used to create effective SER systems for languages with little or no training data, confirming the findings of previous studies. Future work could involve improving SER performance when training on non-target languages and trying to predict which combination of source languages will give the best result.  
Conceptualization, E.A.R. and R.S.; methodology, E.A.R.; software, E.A.R. and E.A.; validation, E.A.R., E.A. and M.M.; formal analysis, E.A.R.; investigation, E.A.R., R.S., A.K., M.M. and E.A.; resources, E.A.R.; data curation, E.A.R.; writing--original draft preparation, E.A.R., J.M., M.A.B. and S.A.C.; writing--review and editing, R.S., S.A.K. and J.M.; visualization, E.A.R. and E.A.; supervision, J.F. and R.S.; project administration, E.A.R. and R.S.; funding acquisition, J.F. and R.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China under grant 2020 YFC1523302.  
Publicly available datasets were analyzed in this study. Our Amharic Speech Emotion Dataset (ASED) is also publicly available at [[https://github.com/Ethio2021/ASED_V1](https://github.com/Ethio2021/ASED_V1)]([https://github.com/Ethio2021/ASED_V1](https://github.com/Ethio2021/ASED_V1)).  
The authors declare no conflict of interest.