## 1 Introduction  
Cleft lip and/or palate (CL/P) is a common congenital anomaly, affecting approximately one in every 700 to 1000 births worldwide [1]. This condition results from incomplete fusion of facial structures during fetal development, leading to a split or opening in the upper lip and/or palate. CL/P severity varies widely, from a minor lip notch to a complete bilateral separation of both lip and palate [1]. Beyond its impact on facial appearance, CL/P significantly affects essential functions like feeding, speech, hearing, and dental development [1]. The interplay of genetic and environmental factors contributes to CL/P's etiology, making early and accurate diagnosis crucial for effective treatment and improved long-term outcomes [1]. Timely intervention, including surgery and rehabilitative therapies, is vital to address both cosmetic and functional impairments, ultimately improving the quality of life for those affected. Building upon our prior research [2], this study extends our investigation into CL/P classification by introducing a novel feature extraction approach. Our previous work [2] combined vision transformers (ViTs) and Siamese neural networks to analyze multimodal data from the UltraSuite CLEFT dataset [3], which includes ultrasound video sequences of tongue movements and synchronized audio recordings. In that study, ViTs captured long-range dependencies and global context within the ultrasound images and spectrograms, while Siamese networks facilitated effective few-shot learning, a critical capability given the limited labeled data in medical imaging [2,4,5]. That approach demonstrated promising results, achieving an overall classification accuracy of 82.76% across the three CL/P types: BCLLP, CP, and UCLP [2]. However, a key limitation of that prior work was its reliance on BiomedCLIP [6] for feature extraction. BiomedCLIP, while effective, is primarily trained on English biomedical text, potentially limiting its ability to capture the full range of nuances in multilingual speech data [7, 8, 9] or the subtle visual details crucial for distinguishing CL/P variations [10, 11, 12, 13, 14, 15, 16]. This paper addresses that limitation by incorporating SigLIP 2 [17], a state-of-the-art multilingual vision-language encoder. SigLIP 2, building upon SigLIP [18] and models like CLIP [19], offers significant advantages. It demonstrates improved semantic understanding, capturing more nuanced relationships between visual and textual information [17,20,21]. Its enhanced localization capabilities allow for more precise identification of relevant image features, crucial for analyzing subtle anatomical variations in CL/P ultrasound images [17]. Furthermore, SigLIP 2's inherent multilingual support makes it better suited for analyzing diverse speech data, a common scenario in CL/P research [17]. SigLIP 2's architecture, with its improved training and larger model sizes, contributes to superior performance in various vision-language tasks [17,20]. We utilize the UltraSuite CLEFT dataset to evaluate our approach. This dataset, designed for CL/P research, provides multimodal data for analyzing speech production in children with cleft conditions. It includes synchronized ultrasound videos of tongue movements and audio recordings. The ultrasound videos provide visual information on tongue articulation, affected by CL/P, while the audio captures acoustic characteristics reflecting potential speech impairments. These complementary modalities, along with textual prompts, enable a comprehensive analysis of speech and articulatory movements relevant to CL/P classification. This study addresses the following research question: Does incorporating SigLIP 2 for feature extraction improve the accuracy and efficiency of CL/P classification compared to the previous ViT-Siamese network model that utilized BiomedCLIP?