# NORESQA: A Framework for Speech Quality Assessment using Non-Matching References

arXiv: https://arxiv.org/abs/2109.08125 · HTML: https://mlsanity.com/paper/2109.08125/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2021.

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

- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [REAL-M: Towards Speech Separation on Real Mixtures](https://mlsanity.com/paper/2110.10812/) (2021)
- [InQSS: a speech intelligibility and quality assessment model using a multi-task learning network](https://mlsanity.com/paper/2111.02585/) (2021)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [MTI-Net: A Multi-Target Speech Intelligibility Prediction Model](https://mlsanity.com/paper/2204.03310/) (2022)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement](https://mlsanity.com/paper/2302.08088/) (2023)
- [Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps](https://mlsanity.com/paper/2407.04578/) (2024)
- [APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech](https://mlsanity.com/paper/2504.20447/) (2025)
- [CORN: Co-Trained Full- And No-Reference Speech Quality Assessment](https://mlsanity.com/paper/2310.09388/) (2023)
- [Residual-Guided Non-Intrusive Speech Quality Assessment](https://mlsanity.com/paper/2203.11499/) (2022)
- [Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions](https://mlsanity.com/paper/2509.24457/) (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)

The perceptual task of speech quality assessment (SQA) is a challenging task for machines to do. Objective SQA methods that rely on the availability of the corresponding clean reference have been the primary go-to approaches for SQA. Clearly, these methods fail in real-world scenarios where the ground truth clean references are not available. In recent years, non-intrusive methods that train neural networks to predict ratings or scores have attracted much attention, but they suffer from several shortcomings such as lack of robustness, reliance on labeled data for training and so on. In this work, we propose a new direction for speech quality assessment. Inspired by human's innate ability to compare and assess the quality of speech signals even when they have non-matching contents, we propose a novel framework that predicts a subjective relative quality score for the given speech signal with respect to any provided reference without using any subjective data. We show that neural networks trained using our framework produce scores that correlate well with subjective mean opinion scores (MOS) and are also competitive to methods such as DNSMOS, which explicitly relies on MOS from humans for training networks. Moreover, our method also provides a natural way to embed quality-related information in neural networks, which we show is helpful for downstream tasks such as speech enhancement.