## 6 Discussion  
The results demonstrate that incorporating SigLIP 2 [17] for feature extraction significantly improves the performance of our CL/P classification model compared to the original model using BiomedCLIP [6]. The overall accuracy increased from 82.76% to 86.67%, with statistically significant improvements in F1 score observed for all three cleft types: CP, UCLP, and BCLP. This confirms our hypothesis that SigLIP 2's enhanced feature representations lead to a more discriminative feature space for CL/P classification. The most substantial improvement was observed for BCLP, with the F1 score increasing from 80.00% to 85.33%. This suggests that SigLIP 2 is particularly effective at capturing the distinctive features of BCLP, which often presents with more pronounced anatomical variations compared to CP and UCLP. Improvements were also seen for CP (F1 score increase from 86.00% to 87.54%) and UCLP (F1 score increase from 82.00% to 85.00%). These consistent improvements across all cleft types highlight the generalizability of the SigLIP 2-based approach. Several factors likely contributed to the performance improvement observed with SigLIP 2. First, SigLIP 2's training on a massive dataset with a sigmoid loss function, as opposed to the contrastive loss used in CLIP [19] and BiomedCLIP, enables it to capture more nuanced relationships between visual and textual concepts [17; 18]. This improved semantic understanding likely allows it to better distinguish subtle differences in the ultrasound images and spectrograms associated with different CL/P types. Second, SigLIP 2's inherent multilingual capability is a significant advantage. While the UltraSuite CLEFT dataset [3] may primarily contain English speech data, the model's ability to generalize across lan \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Class** & **CP** & **Predicted Class UCLP** & **BCLP** \\ \hline CP & 9 & 1 & 1 \\ UCLP & 1 & 9 & 1 \\ BCLP & 0 & 1 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for SigLIP 2 model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Feature Extraction** & **Classification (Per Sample)** \\ \hline Original (BiomedCLIP) & 0.12 & 0.005 \\ New (SigLIP 2) & 0.18 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average computational time (in seconds). guages likely makes it more robust to variations in pronunciation and accent, which can be present even within a single language. BiomedCLIP, being primarily trained on English text, may be less robust to such variations. Third, the refined training strategy employed in SigLIP 2, including larger batch sizes and longer training schedules, contributes to more robust and generalizable feature representations [17]. Finally, SigLIP 2's NAFlex capability allows it to handle images of varying resolutions and aspect ratios more effectively. While we resized images to a fixed input size, the inherent flexibility of NAFlex might contribute to better feature extraction, even after resizing. While direct comparisons with other studies are challenging due to differences in datasets and specific tasks, our results compare favorably with existing work in related areas. [PERSON] et al. [10] achieved 91.10% accuracy in hypernasality detection using speech audio data, but their focus was on a different aspect of speech impairment. Our model achieved a comparable overall accuracy (86.67%) for the more complex task of classifying different CL/P types using multimodal data. Other studies focusing on image analysis, such as those by [PERSON] et al. [11] and [PERSON] et al. [13], primarily address segmentation tasks rather than classification. Our work demonstrates the potential of combining vision-language models with few-shot learning techniques for accurate CL/P classification, a relatively unexplored area. This study has several limitations. The UltraSuite CLEFT dataset, while valuable, is relatively small (29 children).