PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors

2026-06-18

AI Summary

This study addresses the challenge that existing speech quality assessment models struggle to sensitively detect localized pitch accent errors in pitch-accent languages such as Japanese. Focusing specifically on accent correctness—a previously underexplored aspect—the authors construct a controllable synthetic Japanese dataset with systematic accent errors. Building upon self-supervised speech representations, they propose a novel approach incorporating mora-conditional feature fusion, an auxiliary task for accent error localization, pairwise ranking loss, and speaker-invariant training. The resulting model significantly improves the accuracy of ranking accent error severity for both seen and unseen speakers, demonstrating strong alignment with human judgments of accent correctness.

Abstract

Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors. We propose Pitch-Accent-focused Speech Quality Assessment (PASQA), which explicitly targets pitch-accent correctness. To train our model, we construct a controlled Japanese accent-error dataset by changing accent patterns using an accent-controllable text-to-speech system, and compute a pseudo accent-quality score from the accent-error rate. PASQA builds on self-supervised representations and employs mora-conditioned fusion, ranking loss, an auxiliary accent-error localization task, and speaker-invariant training. Experiments show that conventional models fail to preserve the ordering by accent-error severity, whereas PASQA achieves high ordering accuracy on both seen and unseen speakers. Further, PASQA shows stronger agreement with human accent-correctness judgments. The code is available at https://github.com/lycorp-jp/PASQA.

Problem

• pitch accent
• speech quality assessment
• accent error
• MOS prediction
• synthetic speech

Innovation

• pitch accent
• speech quality assessment
• self-supervised representation
• accent-error localization
• speaker-invariant training