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ACR-Net: Mitigating Semantic Dominance via Contrastive Acoustic-Semantic Decoupling

2026-10-14 12:42:55
Mengke Zhang, Yanda Shao, Tianhe Wu, Kai Feng
arXiv_SD
             
Abstract

Speech Emotion Recognition (SER) models and Audio LLMs fail when vocal tone contradicts textual semantics. Extensive training on congruent data engenders a rigid semantic prior, causing "Semantic Dominance", a severe text bias during cross-modal conflicts. We introduce the ASPIRE benchmark of adversarial audio-text pairs across four conflict types, alongside two metrics: Semantic Overconfidence Penalty (SOP) and Latent Decoupling Degree (LDD). Unlike fusion methods that merely re-weight collapsed features, our Acoustic Conflict Resolution Network (ACR-Net) uses Cross-Modal Attention and Contrastive Decoupling Loss to disentangle contradictory representations into orthogonal latent spaces. Experiments show existing models fail under polarity conflicts due to extreme semantic overconfidence. Conversely, ACR-Net preserves acoustic fidelity, maintaining high LDD to drive SOP near zero, achieving superior Acoustic Accuracy (ACC) in both adversarial and standard scenarios.

Abstract (translated)

URL

https://arxiv.org/abs/2610.20114

PDF

https://arxiv.org/pdf/2610.20114.pdf

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