ACR-Net: Mitigating Semantic Dominance via Contrastive Acoustic-Semantic Decoupling
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....
| Authors | Mengke Zhang, Yanda Shao, Tianhe Wu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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