# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 3 Evaluation
### Performance Evaluation  
Precision, Recall, and F1 Score are the main key performance indicators used to compare the performance of various models or algorithms [47]. Precision is the ability of the classifier not to label as positive a sample that is negative, and Recall is the ability of the classifier to find all the positive samples. The F1 Score can be interpreted as a weighted harmonic mean of the Precision and Recall. All of them were computed for the proposed model and other baseline models. In our experiment, the micro-F1 Score was used as the evaluation metric.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Emotion** & **Training Set** & **Test Set** & **Validation Set** \\ \hline Anger & 1109 & 153 & 345 \\ Disgust & 271 & 22 & 68 \\ Fear & 268 & 40 & 50 \\ Joy & 1743 & 163 & 402 \\ Neutral & 4710 & 470 & 1256 \\ Sadness & 683 & 111 & 208 \\ Surprise & 1205 & 150 & 281 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The emotion distribution in MELD.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**MELD Statisic** & **Training Set** & **Test Set** & **Validation Set** \\ \hline No. of modalities & \{a, v, t\} & \{a, v, t\} & \{a, v, t\} \\ No. of unique words & 10643 & 2384 & 4361 \\ Avg./Max utterance length & 8.0/69 & 7.9/37 & 8.2/45 \\ No. of dialogues & 1039 & 114 & 280 \\ No. of dialogues dyadic MELD & 2560 & 270 & 577 \\ No. of utterances & 9989 & 1109 & 2610 \\ No. of speakers & 260 & 47 & 100 \\ Avg. No. of utterances per dialogue & 9.6 & 9.7 & 9.3 \\ Avg. No. of emotions per dialogue & 3.3 & 3.3 & 3.2 \\ Avg./Max No. of speakers per & 2.7/9 & 3.0/8 & 2.6/8 \\ No. of emotion shift & 4003 & 427 & 1003 \\ Avg. duration of an utterance & 3.59 s & 3.59 s & 3.58 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: The detailed distribution of MELD. In the training, validation, and test sets, the average utterance length is almost the same.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Graphics Card** & **Server** & **RAM** \\ \hline NVIDIA GeForce RTX 3090 Ti & AMD Ryzen 9 5950X 16-Core Processor 3.4 GHz & 32 G \\ \hline \hline \end{tabular}
\end{table}
Table 3: The server hardware configuration information.  
For binary classification evaluation metrics, the calculation formula for F1 Score is shown in Equations (13)-(15), as follows:  
\[Recall=\frac{TP}{TP+FN}, \tag{13}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{14}\]  
\[F1=2*Recall*\frac{Precision}{Recall+Precision}, \tag{15}\]  
where \(TP\) is true positives, \(TN\) is true negatives, \(FP\) is false positives, and \(FN\) is false negatives.  
Multiclass evaluation metrics are derived from binary classification evaluation metrics. The micro-F1 Score takes into account the issue of class imbalance. This approach calculates the global Precision and Recall directly based on individual samples. The calculation formulas are shown in Equations (16)-(18), as follows:  
\[Precision_{micro}=\frac{\sum_{i=1}^{L}TP}{\sum_{i=1}^{L}TP+\sum_{i=1}^{L}FP}, \tag{16}\]  
\[Recall_{micro}=\frac{\sum_{i=1}^{L}TP}{\sum_{i=1}^{L}TP+\sum_{i=1}^{L}FN}, \tag{17}\]  
\[micro-F1=\frac{2\cdot Precision_{micro}\cdot Recall_{micro}}{Precision_{micro }+Recall_{micro}}, \tag{18}\]