arXiv AI
DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization
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ConformalMOS: Uncertainty-Aware MOS Prediction with Conformal Intervals and Ordinal Modeling
ConformalMOS is a framework that enhances Mean Opinion Score (MOS) prediction for synthetic speech by adding conformal prediction to generate statistically valid prediction intervals with guaranteed coverage under exchangeability, while also providing point estimates. The method models MOS labels in an ordinal-aware way by converting one-hot targets into Gaussian-smoothed distributions, which preserves score ordering and improves uncertainty calibration, especially when softmax confidence is overconfident on out-of-distribution speech. On BVCC datasets, ConformalMOS achieves competitive accuracy (MSE = 0.08) and empirically validated coverage, thereby improving reliability for real-world TTS and VC deployment.