PASQA predicts pitch-accent quality using synthetic accent-error data, mora-conditioned fusion, ranking loss, and auxiliary localization, outperforming conventional MOS models in ordering by error severity and human agreement.
Experimental setup Dataset preparation.We generate synthetic Japanese speech with controlled pitch-accent errors using NANSY-TTS [21]
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PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors
PASQA predicts pitch-accent quality using synthetic accent-error data, mora-conditioned fusion, ranking loss, and auxiliary localization, outperforming conventional MOS models in ordering by error severity and human agreement.