# Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech

arXiv: https://arxiv.org/abs/2508.08962 · HTML: https://mlsanity.com/paper/2508.08962/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2025.

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

- [AudioMosaic: Contrastive Masked Audio Representation Learning](https://mlsanity.com/paper/2605.14231/) (2026)
- [Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling](https://mlsanity.com/paper/2511.20257/) (2025)
- [SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment](https://mlsanity.com/paper/2602.14785/) (2026)
- [CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across Speech Foundation Models](https://mlsanity.com/paper/2609.14956/) (2026)

## Abstract (arXiv)

Self-supervised learning (SSL) models like Wav2Vec2, HuBERT, and WavLM have been widely used in speech processing. These transformer-based models consist of multiple layers, each capturing different levels of representation. While prior studies explored their layer-wise representations for efficiency and performance, speech quality assessment (SQA) models predominantly rely on last-layer features, leaving intermediate layers underexamined. In this work, we systematically evaluate different layers of multiple SSL models for predicting mean-opinion-score (MOS). Features from each layer are fed into a lightweight regression network to assess effectiveness. Our experiments consistently show early-layers features outperform or match those from the last layer, leading to significant improvements over conventional approaches and state-of-the-art MOS prediction models. These findings highlight the advantages of early-layer selection, offering enhanced performance and reduced system complexity.