# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 6 Conclusions  
The findings of this study confirm that frozen PTMs used solely as feature extractors can achieve high performance in Spanish SER, matching and even surpassing SOTA work, without the need for extensive task-specific fine-tuning. In particular, 24-layer architectures tend to capture crucial acoustic and linguistic features in earlier layers (4-6), while deeper architectures peak at intermediate layers (9-15). Moreover, our approach outperforms previously reported techniques in Spanish SER, attaining F1-scores of 99.83% on INTER1 SP, 88.32% on EM-SpDB, 92.53% on MEACorpus, and 58.92% on EmoWisconsin. Spanish-specific fine-tuned models such as W2V2-L-XLSR53-ES (24 layers) and W2V2-XLSR-ES (48 layers) yielded the best overall scores, indicating the suitability of Wav2 Vec 2.0-based architectures for SER regardless of model depth. Furthermore, TRILLSson's notable generalization performance highlights its strong applicability for speaker-independent and real-world scenarios. Equally important, our results underscore that performance heavily depends on dataset characteristics, including the number of speakers, emotional variability, and whether emotions are natural, induced, or acted, all of which substantially impact the generalization of SER systems in practical environments. Finally, through a multi-dataset, layer-wise, and LOSO-based evaluation, we demonstrated how specific layers provide the richest emotional representations based on architecture size, opening new avenues for efficient, scalable SER solutions in Spanish. Despite the strong performance reported, several limitations remain. First, we did not systematically evaluate computational cost or energy consumption, both of which are critical for real-time or on-device SER. Second, our analysis focused on [PERSON]'s discrete emotional categories, potentially oversimplifying the spectrum of human affects. Third, we relied on the mean-pooling of embeddings, which might overlook salient temporal cues. Moreover, we did not apply formal significance tests (e.g., ANOVA or t-tests) to determine whether performance differences between PTMs are statistically meaningful. Based on the above observations, we outline several key recommendations. First, carefully evaluate the trade-off between freezing and fully fine-tuning large PTMs, as well as the option of adopting more compact architectures (e.g., TRILLsson), which can ease computational overhead without critically sacrificing accuracy. Second, integrate statistical significance tests into system evaluations, thus enabling data-driven decisions about model configurations and ensuring that observed performance differences are both numerically and statistically meaningful. Third, go beyond discrete emotion taxonomies (e.g., [PERSON]'s) by considering more nuanced emotional constructs for a richer perspective on affective states. Finally, carefully plan or select SER databases by balancing the number of speakers, emotional variability, and the nature of the data (acted, induced, or natural), as these aspects could substantially influence performance in real-world scenarios. For future work, we identify three promising directions. One is to extend these insights for truly multilingual SER, integrating or pre-training in languages beyond Spanish and refining cross-lingual generalization. Another direction is to adapt and validate SER systems in real-world scenarios--such as call centers or clinical settings--for practical, scalable solutions. Finally, the development of multi-output models capable of detecting multiple emotional states in the same audio file is needed to address the simultaneous nature of human emotions. Such advancements will propel SER research and broaden its impact across diverse application domains.