## 4 Experiments
### Settings of Image Models  
Emotional recordings from the IEMOCAP and CREMA-D datasets were transformed into visual formats, such as scalograms and 3D Mel-spectrograms. These visual representations enabled the use of pre-trained image classification models like AlexNet, GoogLeNet, SqueezeNet, and ResNet for recognizing emotions from speech. This section describes the settings used to train these source image-based models for TL in SER. Scalograms were generated using continuous wavelet transform filters with Morse and Morlet wavelets, selected for their ability to capture features across varying time and frequency scales [59]. This makes them particularly suitable for analyzing the complex temporal and spectral characteristics of emotional speech. Figure 6 provides examples of scalograms corresponding to different emotional states, demonstrating how these scalograms represent emotions like anger, happiness, neutrality, and sadness. As an alternative method for parameterizing the speech signal, we also utilized 3D Mel spectrograms, known for their strong performance in the SER tasks. These were generated by calculating static, delta (1), and delta-delta (2) coefficients of the Mel spectrogram. The  
Figure 6: Examples of scalograms for different emotional states: (**a**) anger, (**b**) happiness, (**c**) neutral and (**d**) sadness. Color variations indicate energy distribution, with warmer colors (e.g., red and yellow) representing higher energy and cooler colors (e.g., blue) indicating lower energy. delta coefficients were computed along the time axis using a nine-frame section to capture temporal variations. The equations for calculating these coefficients are provided below:  
\[m_{i}^{d}=\frac{\sum_{n=1}^{N}n(m_{i+n}-m_{i-n})}{2\sum_{n=1}^{N}n^{2}}, \tag{1}\]  
\[m_{i}^{dd}=\frac{\sum_{n=1}^{N}n\left(m_{i+n}^{d}-m_{i-n}^{d}\right)}{2\sum_{n =1}^{N}n^{2}}. \tag{2}\]  
The Mel spectrograms were extracted using FFT window size of 512 samples, with 100-sample overlap. Hamming window was applied to each time frame. The Mel-filter banks covered a frequency range from 0 to 8000 Hz. Figure 7 shows the different representations of an emotional utterance labelled as anger, combined to form the resulting 3D Mel spectrogram. We tested the models by progressively transitioning from simpler to more complex CNN architectures, all pre-trained on over a million images from the ImageNet dataset. Table 3 summarizes the key characteristics of these pre-trained networks, including their depth, size, number of parameters, input image size, and accuracy metrics on the ImageNet dataset. This information serves as a basis for selecting the most appropriate model, depending on the specific requirements of the SER task. To ensure a fair comparison across these architectures, we standardized the hyperparameters, as detailed in Table 4. Stochastic Gradient Descent (SGD) was selected as the optimization algorithm due to its balance between accuracy and efficiency. Particular attention was given to tuning the initial learning rate, which is critical for adjusting the model's weights during training. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Depth** & \begin{tabular}{c} **Size** \\ **(MB)** \\ \end{tabular} & \begin{tabular}{c} **Parameters** \\ **(Millions)** \\ \end{tabular} & \begin{tabular}{c} **Input** \\ **Image Size** \\ \end{tabular} & \
\begin{tabular}{c} **Top 1** \\ **Accuracy [\%]** \\ \end{tabular} &
\begin{tabular}{c} **Top 5** \\ **Accuracy [\%]** \\ \end{tabular} \\ \hline AlexNet [36] & 8 & 227 & 61.0 & 227 \(\times\) 227 & 63.30 & 84.60 \\ GoogLeNet [60] & 22 & 27 & 7.0 & 224 \(\times\) 224 & 68.30 & 88.70 \\ SqueezeNet [49] & 18 & 4.6 & 1.24 & 224 \(\times\) 224 & 60.40 & 82.50 \\ ResNet-18 [51] & 18 & 44 & 11.7 & 224 \(\times\) 224 & 73.19 & 90.82 \\ ResNet-50 [51] & 50 & 96 & 25.6 & 224 \(\times\) 224 & 80.67 & 95.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics and performance metrics of image classification models on the ImageNet.