# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 4 Results
### Layer-Wise Evaluation Results
The MESD database also has a high performance with the W2V2-XLSR-ES PTM and MLP classifier, achieving 95.38%\({}_{6}\) in accuracy, 95.40%\({}_{6}\) in F1-score, and 95.38%\({}_{6}\) in UAR. The EmoFilm\({}_{ES}\) database achieves 91.67%\({}_{22}\) in accuracy, 91.52%\({}_{22}\) in F1-score, and 91.83%\({}_{22}\) in UAR, using Whisper-L-v3-ES PTM and the MLP classifier. In contrast, the EmoWisconsin database, due to its complexity and limited emotional range, shows lower and unstable performance. Its best accuracy is 62.50%\({}_{15}\) and is achieved by W2V2-XLSR-ES and SVM, while the highest F1-score is 58.92%\({}_{31}\) and is obtained by Whisper-L-v3-ES and MLP, and the highest UAR is 40.0%\({}_{14}\) and is observed by W2V2-XLSR-ES and MLP. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**PTM**} & \multirow{2}{*}{**Database**} & \multicolumn{3}{c}{**KNN**} & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**SVM**} \\ \cline{3-11}  & & **Accuracy** & **F1** & **UAR** & **Accuracy** & **F1** & **UAR** & **Accuracy** & **F1** & **UAR** \\ \hline \multirow{8}{*}{ \begin{tabular}{c} Whisper- \\ L-v3 \\ \end{tabular}  } & EmoFilm\({}_{ES}\) & 75.0\({}_{12}\) & 74.19\({}_{12}\) & 73.44\({}_{19}\) & **87.5\({}_{15}\)** & 87.17\({}_{19}\) & **86.53\({}_{15}\)** & **87.5\({}_{16}\)** & **87.25\({}_{16}\)** & 86.07\({}_{16}\) \\  & EM-SpDB & 61.6\({}_{7}\) & 59.6\({}_{7}\) & 54.76\({}_{7}\) & **85.29\({}_{20}\)** & **85.29\({}_{20}\)** & **83.87\({}_{14}\)** & 83.79\({}_{20}\) & 83.72\({}_{20}\) & 82.16\({}_{15}\) \\  & EmoWisconsin & 55.39\({}_{18}\) & 50.36\({}_{18}\) & 28.38\({}_{18}\) & 58.33\({}_{18}\) & 56.44\({}_{18}\) & 35.59\({}_{7}\) & **60.05\({}_{18}\)** & 55.520 & 31.5\({}_{15}\) \\  & INTERISP & 91.15\({}_{14}\) & 91.14\({}_{14}\) & 92.27\({}_{14}\) & 99.17\({}_{13}\) & 99.17\({}_{16}\) & 99.38\({}_{16}\) & **99.34\({}_{14}\)** & **99.34\({}_{14}\)** & **99.5\({}_{14}\)** \\  & MESD & 83.24\({}_{11}\) & 83.12\({}_{11}\) & 83.31\({}_{11}\) & **94.8\({}_{7}\)** & **94.79\({}_{7}\)** & **94.83\({}_{7}\)** & 93.06\({}_{13}\) & 93.05\({}_{13}\) & 93.13\({}_{13}\) \\  & MEACorpus & 86.35\({}_{12}\) & 86.15\({}_{12}\) & 83.27\({}_{14}\) & 88.79\({}_{9}\) & 88.73\({}_{9}\) & 87.5\({}_{11}\) & **90.06\({}_{12}\)** & **89.89\({}_{12}\)** & **88.83\({}_{12}\)** \\ \hline \multirow{8}{*}{\ \begin{tabular}{c} W2V2- \\ XLSR- \\ ES \\ \end{tabular}  } & EmoFilm\({}_{ES}\) & 80.56\({}_{7}\) & 79.88\({}_{5}\) & 80.58\({}_{5}\) & 87.5\({}_{6}\) & 86.91\({}_{6}\) & 86.31\({}_{16}\) & **88.89\({}_{11}\)** & **88.88\({}_{11}\)** & **88.27\({}_{11}\)** \\  & EM-SpDB & 66.08\({}_{5}\) & 64.71\({}_{5}\) & 59.79\({}_{3}\) & **86.78\({}_{15}\)** & **86.59\({}_{15}\)** & **84.66\({}_{15}\)** & 85.04\({}_{11}\) & 84.88\({}_{11}\) & 83.53\({}_{15}\) \\  & EmoWisconsin & 58.09\({}_{15}\) & 51.81\({}_{16}\) & 28.56\({}_{1}\) & 60.78\({}_{14}\) & **58.41\({}_{14}\)** & **40.01\({}_{14}\)** & **62.5\({}_{15}\)** & 54.75\({}_{25}\) & 31.15\({}_{15}\) \\  & INTERISP & 96.03\({}_{11}\) & 96.01\({}_{11}\) & 97.02\({}_{11}\) & 99.67\({}_{8}\) & 99.67\({}_{8}\) & 99.75\({}_{8}\) & **99.75\({}_{12}\)** & **99.75\({}_{12}\)** & **99.81\({}_{12}\)** \\  & MESD & 87.28\({}_{7}\) & 86.91\({}_{7}\) & 87.34\({}_{7}\) & **95.38\({}_{6}\)** & **95.46\({}_{9}\)** & **95.38\({}_{6}\)** & 93.64\({}_{8}\) & 93.63\({}_{8}\) & 93.68\({}_{8}\) \\  & MEACorpus & 90.84\({}_{11}\) & 90.74\({}_{11}\) & 89.47\({}_{5}\) & 90.64\({}_{6}\) & 90.59\({}_{6}\) & 90.44\({}_{6}\) & **92.69\({}_{9}\)** & **92.53\({}_{5}\)** & **90.82\({}_{3}\)** \\ \hline \multirow{8}{*}{ \begin{tabular}{c} Whisper-L- \\ v3-ES \\ \end{tabular}