# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Experimental Analysis  
An experimental scenario was designed to evaluate the performance of our proposed method, focusing on three key metrics: accuracy, coverage, and inference speed. The experiment primarily compares two approaches: fixed clip generation and dynamic clip generation. In the first phase, we compare the impact of generating clips with fixed sizes ranging from 2 to 6 frames on the validation dataset. For each configuration, we measure the following:  
* **Average Accuracy**: The model's ability to correctly identify sign language gestures. * **Average Coverage**: The proportion of video frames that contribute to the clip, reflecting how well the clip captures the content of the video on average. * **Average Inference Time**: The total processing time, reflecting the model's overall inference efficiency. The impact of different clip sizes on model performance is visualized in Figure 2, which presents the average accuracy for the fixed clip generation methods. The results demonstrate that increasing the clip size generally improves accuracy across all models; however, beyond a certain clip size, model accuracy either no longer increases or even decreases. From the results, we observe that all models show improved accuracy as the clip size increases. However, after a clip size of 4, the accuracy gains become marginal, indicating a saturation point. Table 10 shows the average coverage values for the different clip sizes. As shown in Table 10, the coverage values increase as the number of clips increases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Number of Clips** & **2** & **3** & **4** & **5** & **6** \\ \hline
3D CNN \& MViT & 0.48 & 0.72 & 0.96 & 1.2 & 1.44 \\ R(2+1)D \& VST & 0.52 & 0.78 & 1.04 & 1.3 & 1.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average coverage values for different clip sizes. Figure 2: Comparison of accuracy across different clip sizes for each model. A more detailed analysis of the trade-off between accuracy and computational efficiency is provided in Figure 3, which illustrates the average inference time for each model at different clip sizes. As shown in Figures 2 and 3, when examining the average accuracy and average inference time for each model at different clip sizes, we observe that, as the clip size increases, the average accuracy generally improves. However, this comes at the cost of increased average inference time. The VST model, while achieving the highest average accuracy, has the slowest average inference time, especially for larger clip sizes. In contrast, the MViT model maintains competitive average accuracy while having a lower average inference time compared to the other models. Based on these observations, selecting an appropriate clip size requires balancing accuracy and real-time processing efficiency in terms of inference time. While increasing the clip size improves accuracy, the gains become marginal beyond a size of 4. Additionally, inference time continues to increase with larger clip sizes, making them less suitable for real-time applications. Therefore, we conclude that a clip size of 4 strikes the best balance between accuracy and computational efficiency. A performance comparison between the proposed dynamic clip generation method and fixed clip generation methods is presented as follows. In the performance evaluation, we compared the performance of our dynamic clip generation method with the fixed method in terms of average accuracy, average coverage, and average inference time. Based on the observed experimental results, at a clip size of 4, the average coverage values are 0.96 for 3D CNNs and MViTs, and 1.04 for R(2+1)D and VSTs, as shown in Table 10. Given these results, we define the optimal coverage threshold as 1. This value ensures a balanced trade-off between capturing meaningful gestures and maintaining efficiency in the processing time. Using this threshold, the dynamic clip generation method adjusts the number of clips to enhance both accuracy and inference speeds.