Constrained CTC Decoding for Efficient Diacritic Restoration
In this work, we address diacritic restoration for Arabic speech transcripts. Most speech data are undiacritized, limiting the ability of modeling fine-grained phonological distinctions. The speech modality has recently been explored as a way to complement text-based diacritic restoration efforts. We propose an efficient non-autoregressive approach for speech-to-text diacritization based on Connectionist Temporal Classification (CTC). Our method incorporates hard constraints during decoding by constructing a character-level diacritization lattice from an undiacritized transcript and restricting hypotheses to valid diacritized realizations. We evaluate on Classical Arabic and Modern Standa...
| Authors | Rufael Marew, Amr Keleg, Hanan Aldarmaki |
|---|---|
| Primary category | cs.CL |
| Published | 21 July 2026 |
| Last updated at source | 28 July 2026 |
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