This paper presents the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized Socio-Political Movement Discourse, hosted at CASE 2025. The task is built on the PrideMM dataset, a curated collection of 5,063 text-embedded images related to the LGBTQ+ pride movement, annotated for four interrelated subtasks: (A) Hate Speech Detection, (B) Hate Target Classification, (C) Topical Stance Classification, and (D) Intended Humor Detection. Eighty-nine teams registered, with competitive submissions across all subtasks. The results show that multimodal approaches consistently outperform unimodal baselines, particularly for hate speech detection, while fine-grained tasks such as target identification and stance classification remain challenging due to label imbalance, multimodal ambiguity, and implicit or culturally specific content. CLIP-based models and parameter-efficient fusion architectures achieved strong performance, showing promising directions for low-resource and efficient multimodal systems.

Nonverbal vocalizations are an essential component of human communication, conveying rich information without linguistic content. However, their computational analysis is hindered by a lack of lexical anchors in the data, compounded by biased and imbalanced data distributions. While disentangled representation learning has shown promise in isolating specific speech features, its application to nonverbal vocalizations remains unexplored. In this paper, we introduce N-CORE, a novel backbone-agnostic framework designed to disentangle intertwined features like emotion and speaker information from nonverbal vocalizations by leveraging N views of audio samples to learn invariance to specific transformations. N-CORE achieves competitive performance compared to state-of-the-art methods for emotion and speaker classification on the VIVAE, ReCANVo, and ReCANVo-Balanced datasets. We further propose an emotion perturbation function that disrupts affective information while preserving speaker information in audio signals for emotion-invariant speaker classification. Our work informs research directions on paralinguistic speech processing, including clinical diagnoses of atypical speech and longitudinal analysis of communicative development. Our code is available at https://github.com/SiddhantBikram/N-CORE.