## 5 Results and Discussion
### Ablation Study
#### 5.3.3 Ablation 3  
In addition, we used the contextual video features collected by [PERSON] et al. [9] as the feature of the video modality, which takes the whole image as the input of ResNet-152 [14]. Then, the text, speech, and video features were fused using the adaptive feature fusion strategy to train the model (\(V^{*}\)). To evaluate the speech modalities' features, we overlapped each window's speech features and used the unaveraged local features to represent the features of the speech modalities. Then, we used MLP to transform the speech elements to the exact dimensions as video and text, and used the unaveraged local features, text features, and face image features to train to obtain Model(A_N). In addition, we extracted global  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Standard Deviation(F1-Score)** \\ \hline Model (w\(\circ\) A) & 74.8 & 74.8 & 74.8 & 0.57 \\ Model (w\(\circ\) F) & 74.9 & 74.3 & 74.2 & 1.31 \\ Ours & **79.0** & **79.0** & **79.0** & **0.54** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiment results. w\(\backslash\)o means removing this component. A represents the data enhancement method, and F represents the feature fusion strategy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Standard Deviation(F1-Score)** \\ \hline Model(w\(\circ\) F) & 74.9 & 74.3 & 74.2 & 1.31 \\ Model(X) & 77.5 & 77.4 & 77.3 & 0.52 \\ Model(\(A_{l}\)) & 76.9 & 76.9 & 76.8 & 1.12 \\ Model(\(TA_{l}\)) & 77.1 & 77.0 & 77.0 & 0.82 \\ Model(\(TAV_{l}\)) & 77.8 & 77.6 & 77.6 & 0.56 \\ Ours & **79.0** & **79.0** & **79.0** & **0.54** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiment results. X represents the fixed \(\alpha\) and \(\beta\) values of 0.01 and 1.0.  
speech features using WavLM [35], a speech model pre-trained by Libri-Light, GigaSpeech, and VoxPopuli, which employs a gated relative positional bias for the Transformer to better capture sequences relation with more giant time steps in the time series. Then, we used this global speech feature and the features of the other two modalities to train the Model(A_O). The experimental results in Table 8 show that replacing face image features with whole image features will reduce the F1-score score (a drop from 79.0% to 77.3%), proving that we should pay more attention to people's facial features information. In addition, we find that the performance of the model(\(V^{*}\)) is 5.0% (from 72.3% to 77.3%) higher than that of the Sequential Context Encoder proposed by [PERSON] et al. [30]. This shows that our adaptive feature fusion strategy is better at discovering inconsistent information between modalities than the encoder-decoder architecture. Using unaveraged speech local features reduces the model's performance by 1.1% (a drop from 79.0% to 77.9%) compared to the averaged features, proving that the averaging operation can highlight sarcasm better. Additionally, using global speech features causes severe performance degradation (Dropped from 79.0% to 73.7%). This shows that global speech features are unfavorable for sarcasm detection because sarcastic speech features have been submerged in global speech features.