## 4 Experiments
### Settings of Speaker Embedding Models  
Speaker embeddings, which capture the unique vocal characteristics of individuals, can be highly effective for tasks such as SER. This section describes the settings used for TL in SER with both baseline and enhanced speaker embedding models.  
In this work, we employed three primary speaker embedding architectures: d-vector, x-vector, and r-vector, along with our enhanced versions of the x-vector and r-vector models proposed in [17]. Following their success on speaker verification, we believe each model offers distinct advantages also in capturing speech features relevant to emotion recognition. Table 5 presents the core attributes of these models, including their depth, size, number of parameters, embedding size, and EER, reflecting their performance. This information serves as a basis for selecting the most appropriate model for the SER task, depending on the specific performance and resource constraints.  
Each speaker model was first trained on the VoxCeleb1 dataset [61], containing a comprehensive collection of real-world speech recordings from a large and diverse group of speakers. This benchmark dataset includes both training and evaluation subsets, ensuring robust model development. Key details about the dataset are summarized in Table 6.  
All audio recordings were processed using a standardized pre-processing pipeline to ensure consistent data quality. This included removing the DC component, applying pre-emphasis filtering with a coefficient of 0.97, and using Hamming window on each speech frame. The speech utterances were segmented into 25 ms frames with a 10 ms overlap, and an 80-dimensional vector of filter bank (FB) features was computed for each frame,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **VoxCeleb1** \\ \hline \# of speakers (Total) & 1251 \\ \# of speakers (Training) & 1211 \\ \# of speakers (Evaluation) & 40 \\ \# of utterances (Total) & 153,516 \\ \# of utterances (Training) & 148,642 \\ \# of utterances (Evaluation) & 4874 \\ Average of utterances per speakers & 116 \\ Average length of utterances (s) & 8.20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the VoxCeleb1 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Optimization algorithm & SGD \\ Momentum & 0.9 \\ Initial learning rate & \(1\times 10^{-3}\) \\ L2 Regularization & \(1\times 10^{-4}\) \\ Epochs & 100 \\ Batch size & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters used in the experiments for image classification models.  
\begin{table}
\begin{tabular}{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} **Embedding** \\ **Size** \\ \end{tabular} & **EER [\%]** \\ \hline d-vector & 5 & 12.6 & 7.5 & 1024 & 6.44 \\ x-vector (baseline) & 9 & 25.8 & 8.5 & 512 & 3.98 \\ Enhanced x-vector (proposed) & 13 & 37.4 & 9.0 & 512 & 3.53 \\ r-vector (baseline) & 9 & 31.2 & 8.0 & 256 & 3.18 \\ Enhanced r-vector (proposed) & 9 & 36.9 & 8.5 & 256 & 2.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics and performance metrics of speaker embedding models on the VoxCeleb1.  
capturing essential spectral information needed for accurate speech analysis. Additionally, energy-based voice activity detection was used to filter out silent frames, further enhancing the dataset's robustness. The training process for the speaker embedding models was configured using specific hyperparameters, as summarized in Table 7. These settings were applied for optimizing the performance of both the baseline and enhanced models.