## 4 Results  
### Speech Emotion Datasets and Experimental Setup  
In this work, our approach was evaluated using two distinct datasets, each offering unique characteristics for assessing affective communication analysis across different languages and emotional expressions.  
The EmoUERJ dataset [36] consists of 377 audio recordings in Portuguese, encompassing four distinct emotional categories: happiness, anger, sadness, and neutral. These recordings were obtained from eight actors, evenly split between male and female, who each contributed ten sentences selected from a set of daily routine phrases. This dataset provides a valuable resource for understanding emotional expression within a specific cultural and linguistic context.  
On the other hand, the Emotional Speech Dataset (ESD) [37] is a multilingual dataset comprising 350 parallel utterances across five emotions: anger, sadness, surprise, neutral, and happiness. Recorded by 10 native English and 10 native Chinese speakers, with equal representation of genders, this dataset allows for a comparative analysis of emotional expression across different languages. For this study, we focused solely on the English portion of the ESD.  
To assess the performance of our models in a more comprehensive manner, we combined the EmoUERJ (Portuguese) and ESD (English) datasets into a multilingual dataset. This cross-linguistic approach allowed for us to evaluate how effectively our framework captures emotional patterns across different languages and cultures. To train and validate our classifiers, we employed a standard 80/20 split strategy (train/test) for both individual and combined datasets.  
Our experimental setup utilized a MacBook Pro M1 Max, equipped with a 10-core CPU (8 performance cores and 2 efficiency cores), a 32-core GPU, and 64 GB of unified memory. This configuration provided efficient parallel processing capabilities, accelerating training and inference for our machine learning models. The EmoUERJ and ESD datasets were both trained and tested on this setup, utilizing the hardware acceleration for optimal performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classifier** & **Configuration/Parameters** & **Notes** \\ \hline \multirow{4}{*}{GPT-2} & Fine-tuned for sequence classification & \\  & 3 labels & \\  & AdamW optimizer & \\  & Cross-entropy loss & \\  & 350 epochs, batch size = 32 & \\  & Linear learning rate scheduler with warmup & \\ \hline \multirow{2}{*}{LR} & Default parameters, max iterations = 1500, & Simple and interpretable linear model. \\  & TF-IDF features & \\ \hline SVM & Linear kernel, C = 1.0, TF-IDF features & Effective for high-dimensional text data. \\ \hline RF & 100 decision trees, TF-IDF features & Ensemble method combining multiple decision trees. \\ \hline \multirow{4}{*}{MLP} & 2 hidden layers (320, 192 neurons) & \\  & Adam optimizer & Neural network for non-linear modeling. \\  & TF-IDF features & \\ \hline \multirow{4}{*}{1D-CNN} & 1 convolutional layer & \\  & 128 filters, kernel size = 3 & \\  & Max-pooling layer (pooling size = 2), & \\  & Flattened output into dense layer with softmax & Adapted for sequential text input. \\  & activation, & \\  & 350 epochs, batch size = 32 & \\ \hline \multirow{4}{*}{DBMM} & 3 Classifiers: BERT, SVM, RF & \\  & 2 time-slices (previous priors) & A grid search optimization is employed to find the \\ \cline{1-1}  & Entropy-based weights & optimal number of time slices (past predictions) to \\ \cline{1-1}  & Bayesian update of weights & incorporate as prior information. \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._