# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Experimental Analysis
Table 11 presents a comparison between the dynamic method and the fixed method, which achieved the highest accuracy for each model. The table highlights key metrics, including the average number of clips generated, inference time (in seconds), inference time reduction rate, and clip reduction rate for each model. Figure 5: Comparison of average accuracy between the dynamic and fixed methods across different models. Figure 6: Comparison of average inference time between the dynamic and fixed methods across different models. It is important to note that although the number of clips is dynamically adjusted depending on the length of the video, each clip still contains a fixed number of frames (e.g., 16 or 30). As a result, shorter videos generate fewer clips, reducing the total number of frames passed through the model and effectively decreasing inference time. For 3D CNNs, the dynamic method achieves a 12.50% reduction in inference time, with an average inference time of 0.21 s compared to 0.24 s for the fixed method. This reduction is accompanied by a slight decrease in the number of clips, with the dynamic method generating an average of 4.37 clips, compared to 5 clips for the fixed method. This suggests that by dynamically adjusting the number of clips based on video content, the dynamic method offers a more computationally efficient solution without significantly compromising accuracy. Similarly, R(2+1)D shows a notable 27.78% reduction in inference time, decreasing from 0.054 s for the fixed method to 0.039 s for the dynamic method. In this case, the dynamic method generates slightly fewer clips (4.22 clips) compared to the fixed method (6 clips), demonstrating the method's efficiency in optimizing clip generation without sacrificing performance. For VSTs, the dynamic method achieves a 28.57% reduction in inference time, from 0.49 s for the fixed method to 0.35 s with dynamic clip generation. The number of clips generated in the dynamic approach (4.22 clips) is slightly lower than in the fixed approach (6 clips), further improving computational efficiency while maintaining high accuracy. However, for MViTs, both the fixed and dynamic methods result in the same inference time of 0.14 s. The number of clips generated by both methods remains the same (4.37 clips for the dynamic method and 4 clips for the fixed method), but the dynamic method's clip reduction rate is negative (\(-9.25\%\)), suggesting that the dynamic method does not provide the same level of improvement for MViTs as it does for other models. In summary, the dynamic clip generation method significantly reduces inference times for three out of the four models (3D CNNs, R(2+1)D, and VSTs), while also reducing the number of clips needed for video recognition. These results demonstrate that the dynamic method offers a substantial computational efficiency advantage in sign language recognition tasks, especially for models that benefit from flexible clip generation. Overall, our results demonstrate that the dynamic clip generation method based on our proposed coverage metric delivers significant advantages across multiple models. Particularly impressive results were observed with R(2+1)D and VST, where we achieved inference time reductions of 27.78% and 28.57% respectively, while maintaining or even improving accuracy. By adjusting the number of clips according to each video's temporal complexity, this method prevents computational waste while ensuring sufficient information capture. Our approach is particularly valuable for real-time video recognition applications where processing speed is critical without compromising recognition performance.