# Multi-Sampling-Frequency Naturalness MOS Prediction Using Self-Supervised Learning Model with Sampling-Frequency-Independent Layer

arXiv: https://arxiv.org/abs/2507.14647 · HTML: https://mlsanity.com/paper/2507.14647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [AnimeScore: A Preference-Based Dataset and Framework for Evaluating Anime-Like Speech Style](https://mlsanity.com/paper/2603.11482/) (2026)
- [ZONOS2 Technical Report](https://mlsanity.com/paper/2606.24320/) (2026)

## Abstract (arXiv)

We introduce our submission to the AudioMOS Challenge (AMC) 2025 Track 3: mean opinion score (MOS) prediction for speech with multiple sampling frequencies (SFs). Our submitted model integrates an SF-independent (SFI) convolutional layer into a self-supervised learning (SSL) model to achieve SFI speech feature extraction for MOS prediction. We present some strategies to improve the MOS prediction performance of our model: distilling knowledge from a pretrained non-SFI-SSL model and pretraining with a large-scale MOS dataset. Our submission to the AMC 2025 Track 3 ranked the first in one evaluation metric and the fourth in the final ranking. We also report the results of our ablation study to investigate essential factors of our model.