SCOREQ: Speech Quality Assessment With Contrastive Regression

Abstract

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

Stats

• citations11
• S2 citations—
• github stars0
• HF likes0
• heat score8.09
• arxiv keyragano2024scoreq

Related papers

• More For Less: Non-intrusive Speech Quality Assessment With Limited Annotations (2021)7.16
• A Comparison Of Deep Learning MOS Predictors For Speech Synthesis Quality (2022)6.34
• Squid: Measuring Speech Naturalness In Many Languages (2022)9.41
• Preference-based Training Framework For Automatic Speech Quality Assessment Using Deep Neural Network (2023)5.24
• Using RLHF To Align Speech Enhancement Approaches To Mean-opinion Quality Scores (2024)0.00
• Partial Rank Similarity Minimization Method For Quality MOS Prediction Of Unseen Speech Synthesis Systems In Zero-shot And Semi-supervised Setting (2023)2.26
• Ccatmos: Convolutional Context-aware Transformer Network For Non-intrusive Speech Quality Assessment (2022)5.24
• Metricnet: Towards Improved Modeling For Non-intrusive Speech Quality Assessment (2021)0.00