## 6 Discussion
Table 11 compares the obtained results on enhanced r-vector and x-vector models with existing studies. For instance, [PERSON] et al. [44] used a pre-trained ResNet34 model with log-Mel spectrograms' input representations, achieving a UA of 64.14% and a WA of 63.61%. The approach by [PERSON] and [PERSON] [42], which utilized acoustic low-level features and a fine-tuned ResNet, yielded a UA of 66.40% but a lower WA of 61.80%. [PERSON] et al. 's [43] method, incorporating a ResNet20 with a residual adapter, significantly improved performance, reaching a UA of 67.58% and a WA of 72.73% on improvised data. [PERSON] et al. [64] fine-tuned ResNet34 on spectrograms resulted in a UA of 65.97% and a WA of 65.40%. Figure 8: The results of the best models, showing baseline performance, individual increments in accuracy due to TL, augmentation, and the proposed enhancements. In comparison, our r-vector model outperforms these existing approaches, achieving a UA of 71.08% and a WA of 70.97% on a mixed set of scripted and improvised data from IEMOCAP. These results demonstrate that the proposed enhancements, together with the TL approach, effectively improve model's abilities to classify emotional states from speech. The obtained results highlight the potential of the enhanced r-vector model as a promising method in the field of SER. Limitations and Future WorkDespite the significant advancements in SER field, several limitations hinder its effectiveness in real-world applications:  
Social desirability can influence how emotions are expressed in speech, as individuals may consciously or unconsciously adjust their tone or manner of speaking to align with socially acceptable norms or expectations. This masking of true emotions can reduce the accuracy of SER systems, as they rely on vocal cues to detect underlying emotions. Moreover, certain emotions may produce similar acoustic patterns, which can lead to difficulties in distinguishing between them based solely on speech features. For instance, emotions like frustration and anger might have overlapping vocal characteristics, complicating the task of accurate classification by SER systems. SER systems that are developed under controlled conditions often struggle to maintain their accuracy in real-world settings. Background noise, overlapping voices, and suboptimal microphone quality are prevalent in practical applications, all of which can degrade system performance. Techniques such as noise reduction and speech enhancement methods could be explored to increase system reliability in diverse environments. Many SER models are tested on a limited number of datasets, which can restrict their generalizability across various domains or speech environments. Emotional expression through speech varies across different cultures and languages, creating challenges for the development of universally applicable SER systems. What might be a typical vocal expression of anger in one culture could differ from another, which complicates the model's ability to accurately detect emotions across diverse populations. Future work should aim to evaluate SER models on a broader range of datasets, encompassing different emotional contexts, languages, and cultural settings. Datasets such as RAVDESS, MSP-IMPROV, or MELD could be employed to assess model performance across more diverse populations and environments, helping to improve the generalizability and adaptability of SER systems. Efforts should be made to develop culturally and linguistically adaptive SER systems. This could involve training models on multilingual and multicultural datasets to enhance the system's ability to recognize emotions across diverse populations. Future improvement strategies could enhance the robustness and generalizability of the SER system. One key challenge that remains unaddressed is the entanglement of speaker characteristics with emotional content, which may result in biased models that focus more on speaker identity than emotion.