Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations
The rapid proliferation of AI-Generated Content (AIGC) has necessitated robust metrics for perceptual quality assessment. However, automatic Mean Opinion Score (MOS) prediction models are often compromised by data scarcity, predisposing them to learn spurious correlations-- such as dataset-specific acoustic signatures-- rather than generalized quality features. To address this, we leverage domain adversarial training (DAT) to disentangle true quality perception from these nuisance factors. Unlike prior works that rely on static domain priors, we systematically investigate domain definition strategies ranging from explicit metadata-driven labels to implicit data-driven clusters. Our findin...
| Authors | Kuan-Tang Huang, Chien-Chun Wang, Cheng-Yeh Yang et al. |
|---|---|
| Primary category | eess.AS |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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