## 2 Related Work  
Over the last two decades, much important research has been conducted on speaker-independent SER. This work has shown that several factors influence accuracy, includingthe dataset utilized, the features extracted, and the classifier network employed to predict emotions. [PERSON] et al. [11] present a thorough survey of SER work. However, while there has been preliminary research on enhancing the robustness of SER by combining multiple emotional speech corpora to form the training set and thereby minimizing data scarcity, there is a shortage of studies on multilingual cross-corpus SER [6; 12]. In the following, we first summarize related cross-lingual work. After that, we outline multilingual studies. Information about all the research is shown in Table 1.  
Concerning cross-lingual studies, [PERSON] et al. [13] carried out an early study in which they trained an SER classifier on one or more datasets and then tested it on another. In a cross-lingual setting, training on ENT and testing on DES gave the lowest Equal Error Rate for Anger (29.9%). [PERSON] et al. [9] proposed an SER classifier for emotion detection, focusing on emotion identification in unknown languages. The results showed what could be expected from a system trained with a different language, reaching 45% on average. The standard multi-class SVM performed better than the classifier implemented using Emotion Profiles (EP). The Standard Classifier (SC) reached 56.8%, whereas the Emotional Profile Classifier (EPC) obtained 52.1%. [PERSON] et al. [14] examined SER for Mandarin Chinese vs. Western languages such as German and Danish. The authors concentrated on gender-specific SER and attained classification rates that were higher than chance but lower than baseline accuracy. The best classification rate in the cross-language family test on male speech samples (71.62%) was when the Chinese Dual-mode Emotional Speech Database (CDESD) was used for training and Emo-DB was used for testing. [PERSON] et al. [15] utilized language detection to improve cross-lingual SER. They found that using a language identifier followed by network selection, rather than a network trained on all existing languages, was superior for recognizing the emotions of a speaker whose language is unknown. On average, the Language IDentification (LID) approach for selecting training corpora was superior to using all of the available corpora when the spoken language was not known. [PERSON] et al. [16] proposed Deep Belief Networks (DBNs) for cross-corpus SER and evaluated them in comparison with MLP via emotional speech corpora for Arabic (KSUEmotions) and English (EPST). Training on one dataset and testing on the other yielded similar results for both directions and both models. The best result was Arabic\(\rightarrow\)English using DBNs (valence 53.22%, arousal 57.2%). [PERSON] et al. [17] extracted eGeMAPS features from their raw audio data. They used SVM with a Gaussian kernel to classify data into their respective categories. The best result came from training on EMO-DB and then testing on URDU (57.87%). [PERSON] et al. [18] also used eGeMAPS features, and they employed five different corpora for three different languages to investigate cross-corpus and cross-language emotion recognition using Deep Belief Networks (DBNs). IEMOCAP performed well on EMO-DB compared to FAU-AIBO even though both of the latter datasets are German. [PERSON] et al. [19] studied SER using languages from various language families, such as Urdu vs. Italian or German. The best cross-lingual results were obtained by training on URDU and testing on EMODB (65.3%) and the worst were by training on URDU and testing on SAVEE (53.2%). [PERSON] et al. [20] used transfer learning to carry out multi-task learning experiments and discovered that traditional machine learning architectures [5; 21] can perform as well as deep learning neural networks for SER provided the researchers pick appropriate input features. Training the model on IEMOCAP and testing it on EMO-DB obtained the best performance (65%).