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.
Figures & tables
Figure 1: An illustration of Semantic Dominance in emotional conflict. Traditional multi-modal models blindly trust the semantic content, whereas our approach detects the acoustic-semantic contradiction.
Conflict Type
Retention
Characteristics &
(Example)
Rate
Synthesis Difficulty
Polarity Opposite (Sad + Happy)
45%
Hardest. Extreme polarity clash; text semantics heavily overpower acoustic prompts.
Arousal Opposite (Neutral + Angry)
84%
Easiest. Neutral acoustics are stable, but high-arousal acoustics easily collapse.
Bipolar. Simulates suppression. Easy for low-arousal text, hard to mask positive text.
Table 1: Structural Categorization of Conflict Pairings in ASPIRE.
Representation Strategy
ACC (%) ↑
SOP ↓
LDD ↑
Naive Concatenation
41.2
0.824
0.108
Advanced Fusion Mechanisms
+ Tensor Fusion [ 23 ]
43.8
0.776
0.115
+ MCR (Re-weighting) [ 16 ]
48.5
0.628
0.242
Explicit Decoupling Interventions (Ablation)
+ CMA (w/o Decouple Loss)
53.0
0.534
0.334
Table 2: Comparison of Representation Strategies on the ASPIRE Benchmark. ACR-Net explicitly disentangles features, achieving the highest ACC while minimizing SOP. The hyperparameter analysis shows the necessity of balanced decoupling.
Table 3: Acoustic Accuracy (ACC %) comparison on standard congruent datasets.
Conflict Type
Model
ACC ↑
SOP ↓
LDD ↑
Polarity Opposite (Sad + Happy)
MCR
35.2
0.612
0.205
FAS
42.5
0.548
0.288
ACR-Net (Ours)
68.4
0.185
0.812
Arousal Opposite (Neutral + Angry)
MCR
65.8
0.315
0.322
FAS
68.4
0.280
0.395
ACR-Net (Ours)
85.2
0.092
0.885
Table 4: Diagnostic Evaluation on the ASPIRE Benchmark. The table details the performance across four distinct structural conflict types. By maintaining a high Latent Decoupling Degree (LDD), ACR-Net effectively suppresses Semantic Overconfidence (SOP) and achieves state-of-the-art Acoustic Accuracy (ACC).