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eess.AS2026
DNSMOS-C: Improving End-to-end Speech Quality Models via Contrastive Learning
Xinyu Liang, Fredrik Cumlin, Victor Ungureanu +3
We introduce DNSMOS-C, a compact end-to-end speech quality assessment model that extends the DNSMOS Pro framework by integrating a MOS-guided triplet-based contrastive loss. Applie…
eess.AS2026
SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment
Fengyuan Cao, Xinyu Liang, Fredrik Cumlin +4
Designing a speech quality assessment (SQA) system for estimating mean-opinion-score (MOS) of multi-rate speech with varying sampling frequency (16-48 kHz) is a challenging task. T…