# SESQA: semi-supervised learning for speech quality assessment

arXiv: https://arxiv.org/abs/2010.00368 · HTML: https://mlsanity.com/paper/2010.00368/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [Universal Speech Enhancement with Score-based Diffusion](https://mlsanity.com/paper/2206.03065/) (2022)
- [CDPAM: Contrastive learning for perceptual audio similarity](https://mlsanity.com/paper/2102.05109/) (2021)
- [MaskSR: Masked Language Model for Full-band Speech Restoration](https://mlsanity.com/paper/2406.02092/) (2024)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility](https://mlsanity.com/paper/2409.09357/) (2024)
- [On tuning consistent annealed sampling for denoising score matching](https://mlsanity.com/paper/2104.03725/) (2021)
- [Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities](https://mlsanity.com/paper/2206.13272/) (2022)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Dataset](https://mlsanity.com/paper/2209.06358/) (2022)
- [CORN: Co-Trained Full- And No-Reference Speech Quality Assessment](https://mlsanity.com/paper/2310.09388/) (2023)
- [JSQA: Speech Quality Assessment with Perceptually-Inspired Contrastive Pretraining Based on JND Audio Pairs](https://mlsanity.com/paper/2507.11636/) (2025)
- [PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets](https://mlsanity.com/paper/2606.19597/) (2026)

## Abstract (arXiv)

Automatic speech quality assessment is an important, transversal task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen recording conditions, and a lack of flexibility of existing approaches. In this work, we tackle these problems with a semi-supervised learning approach, combining available annotations with programmatically generated data, and using 3 different optimization criteria together with 5 complementary auxiliary tasks. Our results show that such a semi-supervised approach can cut the error of existing methods by more than 36%, while providing additional benefits in terms of reusable features or auxiliary outputs. Improvement is further corroborated with an out-of-sample test showing promising generalization capabilities.