# Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations
## 6 Results and Discussion  
### Experiment 1: Cross-Corpus Strategy for Training  
The first experiment examines the viability of cross-corpus strategies for improving the performance of SER systems. The impact of utilizing different combinations of classifiers and feature extractors from multiple datasets was investigated. The experiment aimed to evaluate the effectiveness of both matched training, where only the test dataset was used for training, and extended training, where all available datasets were used for training. The results of the cross-corpus experiments are presented in Table 5. The performance metrics for each combination of datasets, classifiers, and representations were computed and analyzed.  
The obtained results reveal a discrete improvement of accuracy in terms of UAR score for the RAVDESS and IEMOCAP datasets when cross-corpus training is applied. Despite this being a discrete improvement, the positive tendency aligns with the conclusions reported in the previous works of [PERSON] et al. [7] about the positive impact of a cross-corpus strategy in emotion recognition. Note that no significant improvement was observed for the EmoDb database. This could be attributed to the limited amount of audio available in the EmoDb dataset.  
Nevertheless, the little difference among the results with and without cross-corpus suggests that further research is needed to clarify the advantage of extending the train set with audio out-domain.  
Furthermore, these results support the previous findings reported in the work by [PERSON] et al. [8], which showed that training DNN-based systems with datasets containing multiple languages leads to performance improvements. This suggests that, despite the linguistic differences, augmenting the training data can help the model generalize better across languages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**MATCHED TRAIN**} & \multicolumn{2}{c}{**EXTENDED TRAIN**} \\ \cline{2-5}  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \hline \multicolumn{5}{c}{Evaluation dataset: EmoDb} \\ \hline MLP & 87.86\% & 85.64\% & 84.64\% & 84.64\% \\ CNNSelfAtt & 87.34\% & 89.70\% & 87.26\% & 87.02\% \\ Transformer & 90.60\% & **90.64\%** & 81.16\% & 79.44\% \\ \hline \multicolumn{5}{c}{Evaluation dataset: RAVDESS} \\ \hline MLP & 68.34\% & 70.82\% & 64.14\% & 65.70\% \\ CNNSelfAtt & 62.44\% & 67.04\% & 68.88\% & **70.92\%** \\ Transformer & 64.78\% & 66.58\% & 69.26\% & 69.14\% \\ \hline \multicolumn{5}{c}{Evaluation dataset: IEMOCAP} \\ \hline MLP & 63.52\% & 63.38\% & 63.38\% & 63.21\% \\ CNNSelfAtt & 64.94\% & 65.90\% & 65.75\% & **66.90\%** \\ Transformer & 60.82\% & 62.08\% & 61.90\% & 62.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy in terms of UAR for the SER system using SS representations (HuBERT and WavLM) with DNN-based classifiers. Matched train: database for training and test is the same; extended training: database for training is IEMOCAP + RAVDESS + EmoDb. Bold text indicates the best performance for each database.