# Constrained CTC Decoding for Efficient Diacritic Restoration

arXiv: https://arxiv.org/abs/2607.18946 · HTML: https://mlsanity.com/paper/2607.18946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict](https://mlsanity.com/paper/2005.08700/) (2020)
- [ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus](https://mlsanity.com/paper/2303.00069/) (2023)
- [Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts](https://mlsanity.com/paper/2306.01031/) (2023)
- [Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2309.15796/) (2023)
- [Automatic Restoration of Diacritics for Speech Data Sets](https://mlsanity.com/paper/2311.10771/) (2023)
- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis](https://mlsanity.com/paper/2505.20506/) (2025)
- [Diacritic Recognition Performance in Arabic ASR](https://mlsanity.com/paper/2302.14022/) (2023)
- [WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing](https://mlsanity.com/paper/2506.01263/) (2025)
- [Abjad AI at NADI 2025: CATT-Whisper: Multimodal Diacritic Restoration Using Text and Speech Representations](https://mlsanity.com/paper/2510.24247/) (2025)

## Abstract (arXiv)

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 Standard Arabic test sets (namely, ArVoice and ClArTTS) against a more computationally-complex multi-modal diacritic restoration baseline, and show statistically significant reductions in diacritic error rates in both, demonstrating that the proposed approach offers both performance and efficiency gains.