## 4 Experimental Setup  
The experimental setup entails the development and evaluation of a recurrent neural network (RNN) with long short-term memory (LSTM) architecture for emotion recognition using audio datasets. Inspired by [20] work on CNNs for environmental sound recognition, this study extends their approach to emotion recognition. The LSTM model, comprising four LSTM layers, a dropout layer, and a dense layer, aims to effectively identify speech sections with relevant information. Additionally, the study introduces the Gray Wolf Optimizer (GWO) requiring optimization. In terms of model architecture, the CNN-LSTM ensemble incorporates both convolutional and recurrent layers to capture spatial and temporal dependencies in the audio data. Convolutional layers extract relevant features from the raw audio waveforms, while LSTM layers process sequences of features over time. Meanwhile, the SVM serves as a traditional yet robust classifier for emotion recognition tasks, leveraging its ability to find the optimal hyperplane to separate different emotion classes in the feature space. Furthermore, the methodology unfolds in three main stages: feature extraction, feature selection (using GWO), and classification. Feature extraction involves extracting meaningful representations from the raw audio waveforms, such as Mel-frequency cepstral coefficients (MFCCs) or logMel features, to capture acoustic characteristics related to different emotions. Feature selection using GWO aims to identify the most discriminative features for emotion recognition, enhancing the model's performance. Subsequently, the selected features are fed into the classification stage, where SVM and CNNs handle the task of classifying emotions based on the extracted features. Specifically, GWO-SVM, GWO-CNN, and GWO-LSTM approaches are explored, each involving initialization, evaluation, updating the GWO population, and a stopping criterion. Key components include solution representation and fitness function, crucial for feature selection optimization. This com \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Observations**} & \multicolumn{1}{c}{**Dataset-Specific Observations**} \\ \hline Limited Actor Diversity & \begin{tabular}{c} RAVDESS: 24 actors may not fully represent diverse vocal characteristics. SAVEE: Only four male actors may limit diversity and variability. EmoDB: Ten actors may not fully capture wide-ranging vocal characteristics. \\ \end{tabular} \\ \hline Imbalanced Emotional Classes & \begin{tabular}{c} RAVDESS, SAVEE, EmoDB: Some emotions have fewer instances, impacting model performance. \\ \end{tabular} \\ \hline Controlled Recording Conditions & \begin{tabular}{c} RAVDESS, SAVEE, EmoDB: Recordings in controlled studios lack natural variability, affecting generalizability to real-world scenarios. \\ \end{tabular} \\ \hline Limited Contextual Information & \begin{tabular}{c} RAVDESS, SAVEE, EmoDB: A lack of contextual cues in datasets may limit the applicability to real-world scenarios influenced by various factors. \\ \end{tabular} \\ \hline Limited Language Representation & \begin{tabular}{c} RAVDESS: Primarily English, limiting cross-lingual applications. EmoDB: Primarily German, affecting cross-lingual usability. \\ \end{tabular} \\ \hline Limited Emotional Variability & \
\begin{tabular}{c} SAVEE: Four basic emotions may restrict generalizability. EmoDB: Seven discrete emotions may not cover the full spectrum of human emotional experiences. \\ \end{tabular} \\ \hline TESS Advantages &
\begin{tabular}{c} Diverse emotional expressions, large number of actors, naturalistic recording conditions, high-quality recordings, detailed metadata, and multimodal data: the TESS dataset stands out for its richness, naturalness, and comprehensive features, contributing to its reliability and robustness in emotional speech analysis. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Observations and considerations about datasets used.