A study on weakly-supervised training approaches for phoneme-level pronunciation scoring

Jazmín Vidal; Luciana Ferrer

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arXiv · 2605.23593

A study on weakly-supervised training approaches for phoneme-level pronunciation scoring

Jazmín Vidal·Luciana Ferrer

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Abstract

Phoneme-level computer-assisted pronunciation training systems typically rely on phoneme-level annotations, which are costly and scarce. In this work, we investigate whether phoneme-level mispronunciation information can be learned without phoneme-level supervision by exploiting higher-level pronunciation labels. Specifically, we study a weakly supervised setting in which models are trained using only utterance- or word-level pronunciation labels and analyze whether this supervision induces useful phoneme-level score predictions. We further consider a two-stage training scenario in which a model trained only with utterance-level labels is finetuned using a limited number of carefully-selected phoneme-level labeled utterances. We find that, using our proposed architecture and selection process, the two-stage process leads to comparable results to those obtained with full phoneme-level supervision, requiring only a small fraction of phoneme-level labels.

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Jazmín Vidal, Luciana Ferrer. 2026-05-22. A study on weakly-supervised training approaches for phoneme-level pronunciation scoring. https://arxiv.org/abs/2605.23593

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