## 3 Methodology
### Transfer Learning
#### 3.1.2 Models Based on Speaker Embeddings  
Speaker recognition stands as a pivotal aspect of HCI, enabling the discernment of unique vocal signatures that define individuals. In the landscape of modern speaker recognition systems, the advent of DNNs has revolutionized the field, empowering the creation of vector representations of speakers' speech. These representations, commonly known as voiceprints or embeddings, encapsulate intricate information about a speaker's voice within a compact space. One of the foundational approaches in speaker recognition is the i-vector, which models both speaker and channel variability within a unified low-dimensional space. However, recent advancements have led to the development of more sophisticated deep-learned embedding models, such as d-vector, x-vector, and r-vector, which have significantly improved the accuracy and robustness of speaker recognition systems. * D-vector: Initially proposed by [PERSON] et al. [52], the d-vector model uses DNNs to map acoustic features at the frame level, producing latent representations of the speech signal at the last hidden layer. [PERSON] et al. [53] further enhanced this approach by incorporating LSTM layers, resulting in a more robust framework for speaker recognition. The d-vector is suitable for simpler tasks with limited computational resources and dataset size, making it ideal for small-scale speaker identification, where capturing temporal dynamics is important. This framework shows promise in emotion recognition, where it is crucial to capture temporal dependencies. * X-vector: Unlike d-vector, x-vector is a robust solution for large-scale speaker recognition tasks, especially in challenging, noisy environments, though it comes with higher computational costs. Pioneered by [PERSON] et al. [54], the x-vector approach uses TDNNs to extract speaker-specific features from speech. [PERSON] et al. [13] further refined this architecture, producing constant-length x-vectors that encapsulate detailed speaker information. The TDNN layers process speech sequences at the frame level, progressively expanding the temporal context to capture subtle variations in speech, making x-vectors highly effective for speaker recognition. * R-vector: Drawing inspiration from the success of CNNs in image recognition, the r-vector model was introduced by [PERSON] et al. [15]. This model integrates a ResNet-based architecture to create speaker embeddings, which offer a powerful combination of detailed feature representations, robustness to noise, and generalization to unseen speakers. The r-vector excels in modelling temporal dependencies and might be a good option for long-utterance speaker recognition, but its computational demands and complexity make it less practical for real-time applications. In our study, we focused on enhancing the speaker embedding models, specifically the x-/r-vector, to improve their effectiveness in SER. Building on our previous work [17], we introduced several key modifications to the baseline x-/r-vector architectures as follows to optimize their performance. For the x-vector model, we utilized a Factorized TDNN (F-TDNN) architecture [55]. This change involved reducing the dimension of feature maps of the original TDNN layers, which made the model more efficient by reducing the number of parameters and computational demands. Additionally, we added shortcut connections in specific layers (3, 5, 7, and 9) to help the model learn more effectively by improving the flow of gradients during training. We also expanded the temporal context in certain layers to capture a broader range of time-based information, which is crucial for detecting complex emotions. Specifically, the temporal context was modified to capture broader temporal information with contexts such as (t - 2, t + 2) in Layer 1, (t - 2, t, t + 2) in Layer 3, (t - 3, t, t + 3) in Layer 5, and (t - 4, t, t + 4) in Layer 7. Instead of using the traditional pooling method, we opted to replace it with Multi-Head Attention (MHA) Pooling.