# SCOREQ: Speech Quality Assessment with Contrastive Regression

arXiv: https://arxiv.org/abs/2410.06675 · HTML: https://mlsanity.com/paper/2410.06675/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 24 of 24)

- [MuQ-Eval: An Open-Source Per-Sample Quality Metric for AI Music Generation Evaluation](https://mlsanity.com/paper/2603.22677/) (2026)
- [Speech Enhancement Based on Drifting Models](https://mlsanity.com/paper/2604.24199/) (2026)
- [APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech](https://mlsanity.com/paper/2504.20447/) (2025)
- [The Limits of Reference-Free Speech Quality Metrics as Evaluators and Rewards on Modern Text-to-Speech](https://mlsanity.com/paper/2609.13150/) (2026)
- [On the Design of Diffusion-based Neural Speech Codecs](https://mlsanity.com/paper/2504.08470/) (2025)
- [JSQA: Speech Quality Assessment with Perceptually-Inspired Contrastive Pretraining Based on JND Audio Pairs](https://mlsanity.com/paper/2507.11636/) (2025)
- [Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement](https://mlsanity.com/paper/2508.20859/) (2025)
- [QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection](https://mlsanity.com/paper/2509.20679/) (2025)
- [Calibration-Reasoning Framework for Descriptive Speech Quality Assessment](https://mlsanity.com/paper/2603.10175/) (2026)
- [Model as Loss: A Self-Consistent Training Paradigm](https://mlsanity.com/paper/2505.21156/) (2025)
- [Multi-agent Auditory Scene Analysis](https://mlsanity.com/paper/2507.02755/) (2025)
- [Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions](https://mlsanity.com/paper/2509.24457/) (2025)
- [SLD-L2S: Hierarchical Subspace Latent Diffusion for High-Fidelity Lip to Speech Synthesis](https://mlsanity.com/paper/2602.11477/) (2026)
- [Absorbing Discrete Diffusion for Speech Enhancement](https://mlsanity.com/paper/2602.22417/) (2026)
- [Multimodal Confidence Modeling in Audio-Visual Quality Assessment](https://mlsanity.com/paper/2605.01219/) (2026)
- [Repurposing a Speech Classifier for Guided Diffusion-Based Speech Generation](https://mlsanity.com/paper/2606.20457/) (2026)
- [LILAC: An Idempotent Neural Speech Codec](https://mlsanity.com/paper/2608.05727/) (2026)
- [StrixAE: An Intelligent Agent for Audio Enhancement under Complex Distortion Coupling in Real-World Scenarios](https://mlsanity.com/paper/2609.03414/) (2026)
- [Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representations](https://mlsanity.com/paper/2609.04525/) (2026)
- [DriftSE: Speech Enhancement with Generative Drifting](https://mlsanity.com/paper/2609.12252/) (2026)
- [Transcript-Supervised Post-Training of Generative Speech Enhancement on Real Recordings via Reinforce Adjoint Matching](https://mlsanity.com/paper/2609.29405/) (2026)
- [Livin' on a Prior: Likelihood Score Approximation for Inverse Problems](https://mlsanity.com/paper/2609.34446/) (2026)
- [Perceptual Quality Loss or Loss of Perceptual Quality?](https://mlsanity.com/paper/2609.35054/) (2026)
- [A Comprehensive Objective Evaluation of Modern Text-to-Speech for Turkish Using Speech Quality Assessment Models](https://mlsanity.com/paper/2610.06057/) (2026)

## Abstract (arXiv)

In this paper, we present SCOREQ, a novel approach for speech quality prediction. SCOREQ is a triplet loss function for contrastive regression that addresses the domain generalisation shortcoming exhibited by state of the art no-reference speech quality metrics. In the paper we: (i) illustrate the problem of L2 loss training failing at capturing the continuous nature of the mean opinion score (MOS) labels; (ii) demonstrate the lack of generalisation through a benchmarking evaluation across several speech domains; (iii) outline our approach and explore the impact of the architectural design decisions through incremental evaluation; (iv) evaluate the final model against state of the art models for a wide variety of data and domains. The results show that the lack of generalisation observed in state of the art speech quality metrics is addressed by SCOREQ. We conclude that using a triplet loss function for contrastive regression improves generalisation for speech quality prediction models but also has potential utility across a wide range of applications using regression-based predictive models.