# The AudioMOS Challenge 2025

arXiv: https://arxiv.org/abs/2509.01336 · HTML: https://mlsanity.com/paper/2509.01336/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2025.

## Built on by (most cited first, 8 of 8)

- [MuQ-Eval: An Open-Source Per-Sample Quality Metric for AI Music Generation Evaluation](https://mlsanity.com/paper/2603.22677/) (2026)
- [GSRM: Generative Speech Reward Model for Speech RLHF](https://mlsanity.com/paper/2602.13891/) (2026)
- [JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions](https://mlsanity.com/paper/2605.04505/) (2026)
- [SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment](https://mlsanity.com/paper/2602.14785/) (2026)
- [Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations](https://mlsanity.com/paper/2603.16201/) (2026)
- [DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment](https://mlsanity.com/paper/2606.10010/) (2026)
- [DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization](https://mlsanity.com/paper/2608.21176/) (2026)
- [XSQ-AST: An Explainable Audio Spectrogram Transformer Framework for Localising Synthetic Speech Artifacts](https://mlsanity.com/paper/2609.24770/) (2026)

## Abstract (arXiv)

This is the summary paper for the AudioMOS Challenge 2025, the very first challenge for automatic subjective quality prediction for synthetic audio. The challenge consists of three tracks. The first track aims to assess text-to-music samples in terms of overall quality and textual alignment. The second track is based on the four evaluation dimensions of Meta Audiobox Aesthetics, and the test set consists of text-to-speech, text-to-audio, and text-to-music samples. The third track focuses on synthetic speech quality assessment in different sampling rates. The challenge attracted 24 unique teams from both academia and industry, and improvements over the baselines were confirmed. The outcome of this challenge is expected to facilitate development and progress in the field of automatic evaluation for audio generation systems.