## 2 Related Work
[PERSON] et al. [22] presented an ensemble learning approach for cross-corpus machine learning SER, utilizing the SAVEE, URDU, EMO-DB, and EMOVO databases. The method employed three of the most prominent machine learning algorithms, Sequential Minimal Optimization (SMO), Random Forest (RF), and Decision Tree (J48), plus a majority voting mechanism. The ensemble approach was worse than the other classifiers except when training on EMOVO and testing on URDU (62.5%). [PERSON] et al. [23] used prosody prediction and employed eight different corpora for five European languages to investigate cross-lingual and multilingual emotion recognition using Wav2 Vec2 XLSR. The multilingual setup outperformed the monolingual one for all selected European languages, except English, by a very small margin. [PERSON] et al. [24] proposed an SER classifier for emotion detection, focusing on learning emotions, irrespective of culture. They also used 3D Mel-Spectrogram features (henceforth referred to as MelSpec) and employed five different corpora for five languages to investigate cross-lingual emotion recognition using an Attention-Gated Tensor Factorized Neural Network (AG-TFNN). The best result was Fold2\(\rightarrow\)German using a 3D TFNN. In addition, Fold5\(\rightarrow\)Telugu had better performance than Fold4\(\rightarrow\)Hindi, even though both languages are of Indian origin. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref** & **Methods** & **Feature** & **Databases and** & **Expts** & **Classes** \\  & **Employed** & **Extraction** & **Languages** & & \\ \hline \multirow{4}{*}{[13]} & \multirow{4}{*}{SVM} & \multirow{4}{*}{Prosodic} & EMO-DB (German) & \multirow{4}{*}{XM} & \multirow{4}{*}{3} \\  & & & DES (Danish), & & \\  & & & ENT (English), & & \\  & & & SA (Arrikaans) & & \\  & & & RMI (Mandarin, & \\
[9] & SVM, SC, EPC & Prosodic Various & English, & & \\  & & MFCC & Italian Persian, & & \\  & & & Punjabi, Urdu) & & \\
[14] & SMO & Various MFCC & CDESD (Mandarin), & & \\  & & & EMO-DB, DES & & \\  & & & EU-EmoSS (English, & \\
[15] & SVM & Various MelSpec & French, German, & \\  & & & Spanish), VESD & & \\  & & & (Chinese), CASIA & & \\  & & & & (Chinese) & \\  & & & KSUEmotions & & \\
[16] & DBN, MLP & Low-level Acoustic & (Arabic), EPST & & \\  & & & (English) & & \\  & & & SAVEE (English), & & \\
[17] & SVM & eGeMAPS & EMOVO (Italian), & & \\  & & & EMO-DB, & & \\  & & & URDU (Urdu) & & \\  & & & FAU-AIBO (German), & & \\
[18] & DBN & eGeMAPS & IEMOCAP (English) & & \\  & & & EMO-DB, & & \\  & & & SAVEE, EMOVO & & \\
[19] & GAN & eGeMAPS Various & EMO-DB, SAVEE, & & \\  & & & EMOVO, URDU & & \\
[20] & LSTM, LR, SVM & ISO9 & SAVEE, IEMOCAP, & & \\  & & & MASC (Chinese) & & \\ \hline \end{tabular}
\end{table} 
Table 1: Previous work on cross-lingual and multilingual SER (X = cross-lingual, M = multilingual, SVM = Support Vector Machine, SC = Standard Classifier, EPC = Emotional Profile Classifier, SMO = Sequential Minimal Optimization, DBN = Deep Belief Network, MLP = Multi-Layer Perceptron, GAN = Generative Adversarial Network, LSTM = Long Short-Term Memory, LR = Logistic Regression, RF = Random Forest, J48 = Decision Tree). We now consider multilingual approaches in which several datasets in different languages are used for training. In addition to the cross-lingual experiments referred to earlier, [PERSON] et al. [13] also carried out some multilingual work in which they trained on various pairs or triples of datasets chosen from EMO-DB, DES, and ENT and then tested on each of these individually. The best result was obtained by training on all three and testing on EMO-DB (an Equal Error Rate of 20.5%). [PERSON] et al. [17] used four different corpora (SAVEE, EMOVO, EMO-DB, and URDU) for four different languages to investigate multilingual emotion recognition using Support Vector Machines (SVM). When training on EMO-DB, EMOVO, and SAVEE and testing on URDU, a result of 70.98% was achieved, which was higher than any pair of these datasets. [PERSON] et al. [19] also used SAVEE, EMOVO, EMO-DB, and URDU.