# UrgentMOS: Unified Multi-Metric and Preference Learning for Robust Speech Quality Assessment

arXiv: https://arxiv.org/abs/2601.18438 · HTML: https://mlsanity.com/paper/2601.18438/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions](https://mlsanity.com/paper/2605.04505/) (2026)
- [ANCHOR: Autoregressive Non-intrusive Chunk-Ordered Refinement for Joint Multi-Resolution Speech Quality Modeling](https://mlsanity.com/paper/2606.10233/) (2026)
- [PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets](https://mlsanity.com/paper/2606.19597/) (2026)
- [A Comprehensive Objective Evaluation of Modern Text-to-Speech for Turkish Using Speech Quality Assessment Models](https://mlsanity.com/paper/2610.06057/) (2026)

## Abstract (arXiv)

Automatic speech quality assessment has become increasingly important as modern speech generation systems continue to advance, while human listening tests remain costly, time-consuming, and difficult to scale. Most existing learning-based assessment models rely primarily on scarce human-annotated mean opinion score (MOS) data, which limits robustness and generalization, especially when training across heterogeneous datasets. In this work, we propose UrgentMOS, a unified speech quality assessment framework that jointly learns from diverse objective and perceptual quality metrics, while explicitly tolerating the absence of arbitrary subsets of metrics during training. By leveraging complementary quality facets under heterogeneous supervision, UrgentMOS enables effective utilization of partially annotated data and improves robustness when trained on large-scale, multi-source datasets. Beyond absolute score prediction, UrgentMOS explicitly models pairwise quality preferences by directly predicting comparative MOS (CMOS), making it well suited for preference-based evaluation scenarios commonly adopted in system benchmarking. Extensive experiments across a wide range of speech quality datasets, including simulated distortions, speech enhancement, and speech synthesis, demonstrate that UrgentMOS consistently achieves state-of-the-art performance in both absolute and comparative evaluation settings.