AI RESEARCH
Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations
arXiv CS.AI
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ArXi:2603.16201v1 Announce Type: cross 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