Update README.md

README.md

@@ -114,11 +114,17 @@ https://github.com/NLP-UMUTeam/umuteam-speech-emotion
 
 The model was evaluated on the Spanish held-out test set used in the `speech-emotion` toolkit.
 
-| Language | Mode | Accuracy | Weighted Precision | Weighted F1 | Macro F1 |
-|---|---:|---:|---:|---:|---:|
-| Spanish | Speech | 88.1207 | 88.3244 | 88.1357 | 84.4829 |
+### Performance comparison on Spanish emotion recognition
 
-These results correspond to the speech-only Spanish configuration. In the full toolkit, multimodal configurations combining speech and text obtain even higher performance, showing the benefit of integrating acoustic and linguistic information.
+| Configuration | Accuracy | Weighted Precision | Weighted F1 | Macro F1 |
+|---|---:|---:|---:|---:|
+| Speech-only | 88.1207 | 88.3244 | 88.1357 | 84.4829 |
+| Text-only | 77.0204 | 77.0449 | 76.8367 | 69.3886 |
+| Multimodal (Concat) | **90.0682** | **90.2048** | **90.0642** | **87.7455** |
+| Multimodal (Mean) | 88.5102 | 88.6163 | 88.5011 | 84.1653 |
+| Multimodal (Multihead) | 82.6680 | 82.3820 | 82.4600 | 75.5606 |
+
+These results show that speech-based emotion recognition provides strong performance for Spanish emotion analysis, while multimodal approaches combining speech and text achieve even higher robustness and overall performance.
 
 ## How to use