# Small Language Models for Speech Emotion Recognition in Text and Audio Modalities
## 4 Results
### Comparison with Previous Works  
Table 6 provides a comparison of the results of this work with those reported in previous studies. The comparison only includes works that performed a cross-validation similar to ours, which guarantees that the models were evaluated independently of the speaker. This decision was motivated by the fact that the selection of the validation set was found to have a significant impact on accuracy. Some studies validate their results using a single session out of the five into which the dataset is divided, without cross-validation. While this ensures speaker independence, it does not allow for a fair comparison, as the choice of validation session can significantly affect accuracy. In our experiment, we observed that when session 2 was used as the validation set, accuracy was significantly higher than when any other session was used for validation. This variability highlights the importance of using a cross-validation strategy to ensure a more reliable and fair assessment.  
As shown in Table 6, while the proposed ensemble architecture outperforms several recent approaches, it falls below the current state-of-the-art. However, it is crucial to consider the methodology used during the training process when interpreting the results. In our approach, very short training without hyperparameter tuning was carried out to avoid overfitting.  
In contrast, many of the current approaches use much longer training, sometimes without early stopping criteria, combined with exhaustive hyperparameter search. In this sense, three of the approaches with better WA and UA [82; 91; 93] performed meticulous hyperparameter fitting. In addition, another outstanding work [90], which like us relied on a pre-trained model ensemble, performed 100 epochs of training and referred their results  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Year** & **WA (\%)** & **UA (\%)** \\ \hline AA-CBGRU [75] & 2022 & 72.8 & 67.8 \\ TFCNN + SL + Vggish [76] & 2022 & 73.0 & — \\ HD-MFM [77] & 2022 & 72.0 & 73.4 \\ ATFNN [78] & 2023 & 73.8 & 64.5 \\ Wav2 Vec2 and prosody [66] & 2023 & 74.2 & 75.6 \\ CHAN [79] & 2023 & 75.0 & 75.9 \\ MM-EMOR [80] & 2023 & 77.9 & 77.1 \\ BERT + Wav2 Vec2 + CA Aux [81] & 2023 & 78.3 & 79.6 \\ CCTG-NET [82] & 2023 & **88.4** & **89.1** \\ SSL-SF-MoE [83] & 2024 & 73.9 & 72.3 \\ Prosody2 Vec + HuBERT Large [84] & 2024 & 72.4 & 73.3 \\ Vesper (Shiftformer) [85] & 2024 & 73.7 & 74.3 \\ BERT-RoBERTa BiLSTM [86] & 2024 & 74.0 & 74.3 \\ TspCA [87] & 2024 & 74.0 & 74.6 \\ MAF network [88] & 2024 & 75.7 & 75.5 \\ MSER [89] & 2024 & 77.2 & 76.6 \\ Mul-TAB [90] & 2024 & 83.9 & 81.9 \\ Bi-modal Bi-task ERM [91] & 2024 & 82.3 & 83.0 \\ SE3D-CARN [92] & 2024 & 84.0 & 83.0 \\ MVAN [93] & 2024 & 86.9 & 86.6 \\ AMGCN [94] & 2025 & 74.3 & 65.1 \\ AFEA-Net [95] & 2025 & 75.1 & 75.3 \\ DSTM [96] & 2025 & 76.3 & 77.4 \\ MemoCMT [97] & 2025 & 81.9 & 81.3 \\ MTLSER [98] & 2025 & 82.6 & 82.2 \\
**Whisper + OPT with context (Ours)** & 2025 & 82.1 & 83.3 \\ \hline \hline \end{tabular} Note: Bold indicates the highest value.  
\end{table}
Table 6: Comparison with previous research.  
to the best epoch. Similarly, the other approach with high WA and UA [92] performed a 200 epoch training with a scheduled learning rate decay but without early stopping. While these procedures are valid and common in the literature, they can sometimes produce better results on a given benchmark at the cost of reducing the model's ability to generalize to new data.  
On the other hand, the fast convergence of our proposal is a notable advantage over other recent approaches. Some architectures outperform our model by resorting to data augmentation techniques, without which their accuracy drops by more than 5 points [93]. In contrast, our model is able to generalize after processing only half of the dataset once.