## 1 Introduction  
Emotions assist individuals to communicate and to comprehend others' points of view [1]. Speech emotion recognition (SER) is the task of comprehending emotion in a voice signal, regardless of its semantic content [2].  
SER datasets are not available in all languages. Moreover, the quantity and quality of the training data that are available varies considerably from one language to another. For example, when evaluated across several datasets, differences in corpus language, speaker age, labeling techniques, and recording settings significantly influence model performance [3; 4]. This encourages the development of more robust SER systems capable of identifying emotion from data in different languages. This can then permit the implementation of voice-based emotion recognition systems in real time for an extensive variety of industrial and medical applications.  
The majority of research on SER has concentrated on a single corpus, without considering cross-lingual and cross-corpus effects. One reason is that, in comparison to the list of spoken languages, we only have a small number of corpora for the study of speech analysis [5]. Furthermore, even when only considering the English language, accessible resources vary in quality and size, resulting in the dataset sparsity problem observed in SER research. In such instances, learning from a single data source makes it challenging for SER to function effectively. As a result, more adaptable models that can learn from a wide range of resources in several languages are necessary for practical applications.  
Several researchers have investigated cross-corpus SER in order to enhance classification accuracy across several languages. These works employed a variety of publicly accessible databases to highlight the most interesting trends [6]. Even though some research has addressed the difficulty of cross-corpus SER, as described in [PERSON] et al. [6], the challenges posed by minority languages such as Amharic have not been investigated.  
Amharic is the second-largest Semitic language in the world after Arabic, and it is also the national language of Ethiopia [7]. In terms of the number of speakers and the significance of its politics, history, and culture, it is one of the 55 most important languages in the world [8]. Dealing with such languages is critical to the practicality of next-generation systems [9], which must be available for many languages.  
In our previous work [10], we created the first spontaneous emotional dataset for Amharic. This contains 2474 recordings made by 65 speakers (25 male, 40 female) and uses five emotions: fear, neutral, happy, sad, and angry. The Amharic Speech Emotion Dataset (ASED) is publicly available for download ([[https://github.com/Ethio2021/ASED_V1](https://github.com/Ethio2021/ASED_V1)]([https://github.com/Ethio2021/ASED_V1](https://github.com/Ethio2021/ASED_V1)) accessed on 17 October 2023). This dataset allows us to carry out the work reported here.  
The contributions of this paper are as follows:  
* We investigate different scenarios for monolingual, cross-lingual, and multilingual SER using datasets for Amharic and three other languages (English, German, and Urdu).
* We experiment with a novel approach in which a model is trained on data in several non-Amharic languages before being tested on Amharic. We show that training on two non-Amharic languages gives a better result than training on just one.
* We present a comparison of deep learning techniques in these tasks: AlexNet, ResNet50, and VGGE.
* To the best of our knowledge, this is the first work that shows the performance tendencies of Amharic SER utilizing several languages.  
The structure of this paper is as follows: Section 2 presents previous work. Section 3 explains our approach, datasets, and feature extraction methods for SER. Section 4 presents the proposed deep learning architecture and experimental settings. Section 5 describes the experiments and outcomes. Finally, Section 6 gives conclusions and next steps.