Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgments. However, existing methods primarily focus on the final preference decision and provide little explicit supervision for localizing and correcting erroneous content....
Zi-Long Huang, K. Lee, Ze-Zhong Jin et al.· 0 citations
Audio large language models (ALLMs) can understand speech content, yet their ability to use speaker identity for verification remains limited. We propose TS-SP (Two-Stage Speaker Preservation), a parameter-efficient framework for learning speaker-preserving representations and making them accessible to an ALLM's langua...
Jun-Jie Li, Zheng Liang, Zhe Li et al.· 0 citations
This work proposes a novel domain-adaptive dual-gating MoE framework for SDD under unseen attack types and acoustic conditions that significantly outperforms the baseline and demonstrates superior generalization capabilities and efficient design.
Experiments show that latent adversarial refinement can improve perceptual and separation metrics under a reduced sampling budget and apply latent adversarial post-training inspired by Flow2GAN for few-step generation.
Manifold-Constrained Hyper-Connections (mHC) is introduced, reformulat- ing residual paths as a multi-stream evolution where informa- tion is mixed through a doubly stochastic matrix, highlighting its effectiveness for robust speaker representation learning.
Zezhong Jin, Xiaoyu Wang, Zhe Li et al.· 0 citations
Experiments on the MER2026-EmoPrefer Challenge dataset and the error-augmented dataset demonstrate that EAPO improves emotion preference prediction and enhances the robustness of MLLM judges when evaluating fluent descriptions that conflict with the video's multimodal emotional evidence.
Zilong Huang, Junyi Peng, Junjie Li et al.· 0 citations
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