## 3 Methodology
### Datasets
#### 3.2.4 Reducing Volume
Convolutional layers apply filters to identify fundamental cues such as pitch variations and energy shifts, while pooling layers downsample in order to retain key features in an efficient manner. Dense layers map the aforementioned features to emotion labels, thereby enabling CNNs to effectively classify emotions such as happiness, sadness, or anger, based on input speech. Figure 6 presents our proposed architecture. Figure 6 presents our proposed architecture for CNN-based speech emotion recognition. Input audio data undergo MFCC feature extraction to generate frequency-based representations. To enhance data diversity and improve generalization, data augmentation techniques, such as noise addition, increasing volume, reducing volume, and time shifting, are applied. The extracted features are then processed by a CNN architecture, comprising convolutional, max-pooling, and dense layers, to

Figure 5: The block diagram of the MFCC computation. Figure 6: The proposed SER architecture. patterns. Finally, a softmax output layer predicts emotion classes (e.g., anger, happiness, sadness). This framework combines augmentation and deep learning for robust and effective emotion detection. ## 4 Experimental Results

### Tools and Packages

To implement the approaches proposed in this study, we used the following tools and packages:

* Python 3.8: A widely used programming language in machine learning and data science. * Google Colab: An online platform that provides a Jupyter notebook environment for running code, facilitating collaboration, and taking advantage of GPU acceleration. * TensorFlow 2.9 and Keras: Deep learning frameworks that provide high-level APIs for building, training, and optimizing neural networks. * Scikit-learn: A machine learning library for preprocessing, feature scaling, and model evaluation. * NumPy and SciPy: Mathematical libraries for handling arrays, performing matrix operations, and implementing scientific computing. * Librosa: A specialized signal processing library for audio analysis, used to extract Mel Frequency Cepstral Coefficients (MFCCs) and other acoustic features. * SoundFile: A library for reading and writing sound files in various formats, used for pre-processing and data augmentation. By using these tools and packages, we efficiently implemented, trained, and tested our proposed approaches while ensuring computational efficiency and reproducibility. ### Evaluation Metrics

In order to evaluate the performance of the model, four key metrics (accuracy, precision, recall, and \(F1\)-score) were calculated during both the training and testing phases. The mathematical equations of these metrics can be expressed as follows:

\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]

\[Precision=\frac{TP}{TP+FP} \tag{2}\]

\[Recall=\frac{TP}{TP+FN} \tag{3}\]

\[F1-Score=\frac{2\times Recall\times Precision}{Recall+Precision} \tag{4}\]

In this context, true positives (\(TP\)) refer to instances where the model correctly identifies the intended emotion, while false positives (\(FP\)) represent cases where the model incorrectly assigns an emotion that is not present. Similarly, true negatives (\(TN\)) indicate instances where the model accurately excludes an emotion, and false negatives (\(FN\)) denote cases where the model fails to detect an emotion that is present. These metrics collectively provide a comprehensive evaluation of the model's performance by measuring its ability to correctly classify emotions while minimizing errors. ## 5 Results and Discussion

The results of our experiments, presented in Tables 2 and 3, demonstrate the substantial impact of various data augmentation techniques and their combinations on the performance of the SER model for both the Saudi Dialect and BAVED datasets. Performance metrics,including accuracy, recall, precision, and F1-score, were evaluated on a 0-100% scale to ensure clarity and facilitate comparison. The findings highlight the effectiveness of augmentation techniques in enhancing model performance across diverse datasets.