# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 5 Discussion and Comparative Analysis  
### PTMs Against SOTA for SER in Spanish  
The layer-wise results, summarized in Table 7 (the bold values in the table indicate the highest accuracy, F1-score, and UAR in the SOTA for each database), reveal substantial improvements over SOTA benchmarks across multiple datasets, driven by the use of Spanish fine-tuned PTMs. In INTER1 SP, HuBERT-L-1160k surpasses the SOTA accuracy by 1.43%, and is joined by W2V2-L-XLSR53-ES, W2V2-XLSR-ES, WavLM-L, W2V2-L-R-Libri960h, Whisper-L-v3, and Whisper-L-v3-ES, all of which also exceed the SOTA benchmark. In EM-SpDB, W2V2-L-XLSR53-ES achieves a remarkable 19.98% accuracy gain over the SOTA, highlighting the benefits of fine-tuning for Spanish. Whisper-L-v3-ES delivers competitive results in EmoFilm\({}_{ES}\), excelling in datasets with natural emotional expressions, while MESD is the only dataset where the proposed method falls slightly short, with a minimal gap of 0.62% in F1-score. These findings demonstrate that feature extraction techniques with PTMs, in particular those fine-tuned to the target language, consistently outperform traditional approaches on diverse emotional datasets.  
The LOSO validation results, summarized in Table 8, highlight the strong generalization capabilities of TRILLsson and the consistent superiority of Spanish fine-tuned PTMs in challenging evaluation settings. TRILLsson achieves the best accuracy (79.66%) and F1-score (78.48%) in EM-SpDB, outperforming the SOTA benchmark of 68.30%, even from non-LOSO evaluations, a remarkable feat given the stringent LOSO conditions. Similarly, W2V2-L-XLSR53-ES surpasses the SOTA in EmoWisconsin, achieving 54.85% accuracy and demonstrating the effectiveness of fine-tuned embeddings in datasets with induced emotions and high variability. Moreover, TRILLsson also leads in MEACorpus with 75.85% accuracy and 71.64% F1-score, demonstrating its good adaptation in diverse datasets, while W2V2-L-XLSR53-ES and W2V2-XLSR-ES maintain strong performance across other databases, including INTER1 SP and EmoFilm\({}_{ES}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Metric** & **Our Result** & **Our PTM (Layer)** & **SOTA Result** &
\begin{tabular}{c} **SOTA** \\ **Reference** \\ \end{tabular} \\ \hline INTER1 SP & Accuracy & **99.83\%** & HuBERT-L-1160k (6) & 98.40\% & [63] \\  & F1-Score & **99.83\%** & HuBERT-L-1160k (6) & - & - \\  & UAR & **99.88\%** & HuBERT-L-1160k (6) & - & - \\ \hline EmoFilm\({}_{ES}\) & Accuracy & **91.67\%** & Whisper-L-v3-ES (6) & - & - \\  & F1-Score & **91.52\%** & Whisper-L-v3-ES (6) & - & - \\  & UAR & **91.83\%** & Whisper-L-v3-ES (6) & - & - \\ \hline EM-SpDB & Accuracy & **88.28\%** & W2V2-L-XLSR53-ES (6) & 68.30\% & [63] \\  & F1-Score & **88.32\%** & W2V2-L-XLSR53-ES (6) & - & - \\  & UAR & **88.02\%** & W2V2-L-XLSR53-ES (6) & - & - \\ \hline EmoWisconsin & Accuracy & **62.50\%** & W2V2-XLSR-ES (15) & - & - \\  & F1-Score & **58.92\%** & Whisper-L-v3-ES (31) & 40.70\% & [66] \\  & UAR & **40.0\%** & W2V2-XLSR-ES (14) & - & - \\ \hline MESD & Accuracy & 95.38\% & W2V2-XLSR-ES (6) & **96.00\%** & [65] \\  & F1-Score & 95.40\% & W2V2-XLSR-ES (6) & **96.00\%** & \\  & UAR & **95.38\%** & W2V2-XLSR-ES (6) & - & - \\ \hline MEACorpus & Accuracy & **92.69\%** & W2V2-XLSR-ES (5) & - & - \\  & F1-Score & **92.53\%** & W2V2-XLSR-ES (5) & 90.06\% & [68] \\  & UAR & **90.82\%** & W2V2-XLSR-ES (3) & - & - \\ \hline \hline \end{tabular} Note: Bold values represent the highest obtained values.  
\end{table}
Table 7: Summary of best results (layer-wise) and SOTA comparison by database and metric.