## 3 Materials and Methods
### Datasets  
To ensure an exhaustive appraisal of the proposed model, it was tested on five datasets spanning three languages, namely English, Arabic, and German. To comprehensively train models using DL, a certain sample size is required, which this study was not able to meet. Therefore, DA was applied to all of the datasets. The following paragraphs summarize each dataset:  
The RAVDESS is a validated multimodal database that contains emotional speech and song recordings. This database comprises 7356 audio files recorded by 24 professional actors divided equally between men and women. The actors were recorded speaking two linguistically neutral statements while adopting a neutral or standardized accent to minimize the influence of regional variations on the emotional content of the speech. Through having the professional actors record these controlled statements, high-quality emotional portrayals devoid of confounding regional accents were obtained. The RAVDESS provides a substantial corpus of emotional speech and songs for research and development in various fields, such as affective computing. The standardized validation protocols embedded in the dataset aid in its comparative benchmarking and reproducibility across studies. Additionally, the spectrum of emotions conveyed by speech in this dataset encompasses an array of affective states, including, but not limited to, tranquility, satisfaction, elation, melancholy, indignation, trepidation, astonishment, and revulsion [25]. Each expression was associated with a distinct level of emotional intensity, ranging from neutral to normal to strong. The EMO-DB was created by the Technical University of the Berlin Institute of Communication Science [26]. It is a free German emotional speech database that includes 535 context-variable sentences used in everyday communication delivered by ten expert actors (five men and five women) who simulated happiness, anger, anxiety, fear, boredom, disgust, or neutrality. The 48 kHz data were downsampled to 16 kHz. The SAVEE dataset comprises 480 utterances in British English recorded by four male postgraduate students and researchers at the University of Surrey, all of whom were native English speakers aged between 27 and 31 [27]. Seven different emotions were expressed through these utterances (happiness, sadness, surprise, fear, disgust, neutrality, and anger),  
Figure 1: The proposed method. with sentences from the phonetically balanced Texas Instruments/Massachusetts Institute of Technology corpus chosen for each emotion (see Table 1). The IEMOCAP dataset was developed by the Signal Analysis and Interpretation Laboratory (SAII) at the University of Southern California. This database represents a multimodal, multi-speaker collection of data. The dataset spans 12 h and includes videos, audio, face tracking, and text transcriptions of paired performances in which actors tried to elicit a particular feeling from the viewer using a combination of improvisation and prepared scenes. Multiple annotators contributed labels to this IEMOCAP database, classifying the data according to dimensional labels, including valence, activation, and dominance, as well as category labels such as anger, happiness, sadness, and neutrality [28]. The SHEIE dataset is a unique emotional speech dataset developed to test the model proposed in this study. It features real interactions from the realm of higher education and focuses on the instructors. Recognizing the scarcity of datasets comprising genuine interactions, this dataset was designed to address the gap in the existing speech emotion studies. It includes six universal emotions, namely anger, happiness, sadness, excitement, boredom, and neutrality. The data were collected from various synchronous online lectures held by the Computer Science Department at King Abdulaziz University and the Islamic Studies Department at AI Jouf University, which were delivered in both Arabic and English. The dataset contains a total of 20 h and 50 min of speech data from 19 lecture sessions.