# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Experimental Analysis
In short, the dynamic clip generation method we proposed dynamically adjusts the number of clips to ensure that the coverage is 1 when generating clips from sign language videos based on the analysis of the experimental results. Figure 4 shows the distribution of the number of clips generated for different models using the proposed dynamic clip generation method. As shown in the figure, each sign language recognition model using the proposed dynamic clip generation method dynamically generates the number of clips based on the length of the sign language video used for recognition. This variability suggests that adaptively allocating the number of clips based  
Figure 3: Average inference time for each model at different clip sizes. on optimal coverage is a reasonable approach, as it allows the model to assign more clips to complex and longer sign language videos, while using fewer clips for simpler ones. In doing so, this method efficiently balances computational cost and representational effectiveness, ensuring that each sign language video receives an appropriate level of granularity for accurate processing. To further evaluate the effectiveness of dynamic clip generation, we compare its accuracy with that of the fixed clip generation methods for each model. Figure 5 compares the average accuracy between the dynamic and fixed methods across different models. Figure 6 presents the comparison of average inference time between the dynamic and fixed methods across different models. As shown in Figures 5 and 6, for 3D CNNs, the fixed method achieved the highest accuracy at 93.00%, with an inference time of 0.24 s using 5 clips, whereas the dynamic method had a slightly lower accuracy of 92.39%, but improved the inference time to 0.21 s, representing a 12.50% reduction. In R(2+1)D, the fixed method reached a 94.05% accuracy with an inference time of 0.054 s using 6 clips, while the dynamic method achieved a 93.97% accuracy but significantly reduced the inference time to 0.039 s--a 27.78% decrease. For VSTs, when using 6 clips, the fixed method achieved its best performance with 98.62% accuracy and an inference time of 0.49 s. However, the dynamic method not only slightly outperformed it with a 98.67% accuracy but also significantly reduced the inference time to 0.35 s, representing a 28.57% improvement. Lastly, for MViTs, the fixed method achieved its best performance with 4 clips, reaching a 94.66% accuracy and an inference time of 0.14 s. The dynamic method resulted in a slightly lower accuracy of 94.50% with the same inference time, indicating that the dynamic method did not provide any advantage in terms of inference time in this case. When the number of clips is 2, we observe that the accuracy for all models (MViTs, 3D CNNs, R(2+1)D, and VSTs) is at its lowest. This indicates that, with only two clips, the generated clips do not adequately capture the temporal context of the video, resulting in a noticeable decrease in performance. In contrast, when the coverage value exceeds 1, the entire temporal information of the video will have already been used at least once. In the experiment, when the coverage value is greater than 1, the number of generated clips was 4 or more. Beyond this point, further increasing the number of clips does not lead to significant accuracy improvements. In some cases, excessive clip generation may potentially lead to minor accuracy degradation, as observed in our experiments with 3D CNNs, which showed lower accuracy with 6 clips compared to 5 clips, and MViTs, which demonstrated decreased performance with 5 clips compared to 4 clips. In other words, generating too many clips for recognition may not provide any benefit in terms of recognition accuracy and inference efficiency. Figure 4: Distribution of the number of clips generated for different models using the proposed dynamic clip generation method. When comparing average inference times across different models, our results demonstrate significant computational efficiency gains with the dynamic method.