PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets
arXiv:2606.19597v1 Announce Type: cross Abstract: Mean opinion scores (MOS) are widely used for speech quality assessment, yet scalar labels are sensitive to rater variability and listening test differences. This introduces labeling noise, which limits the reliability of MOS prediction. Preference prediction reduces this variability as listeners compare signals directly, producing cleaner labels. We study MOS-free preference prediction and propose PrefSQA, which incorporates uncertainty-aware logits, an impairment attention head, and a module based on non-matching-reference comparisons. We use
The proliferation of AI-generated speech and increasing demands for high-quality audio experiences necessitate more robust and reliable speech quality assessment methods, moving beyond traditional, variable Mean Opinion Scores.
This development offers a more precise, less subjective way to evaluate and improve AI-generated speech and communications, directly impacting product development and user experience across various AI applications.
The shift from scalar MOS labels to pairwise preference prediction and the introduction of advanced models like PrefSQA fundamentally change how speech quality is measured and optimized, enabling cleaner data and more accurate assessments.
- · AI speech synthesis companies
- · Customer service AI platforms
- · Audio software developers
- · Telecommunications providers
- · Traditional subjective listening test methodologies
- · Companies relying on unreliable speech quality metrics
Improved speech quality assessment leads to more refined and natural-sounding AI voices.
Higher quality AI speech enhances user trust and adoption rates for voice interfaces and automated services.
The increased sophistication of AI speech could blur the lines between human and synthetic voices, potentially impacting identity verification and content authenticity.
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Read at arXiv cs.LG