Problem
Research questions and friction points this paper is trying to address.
diacritic restoration
Arabic speech transcripts
phonological distinctions
speech-to-text diacritization
Constrained CTC Decoding for Efficient Diacritic Restoration
🤖 AI Summary
This work addresses the challenge of diacritic omission in Arabic speech transcription, which obscures fine-grained phonetic distinctions and hinders accurate modeling. The authors propose a CTC-based non-autoregressive approach for diacritic restoration that constructs a character-level diacritic lattice and enforces hard constraints during decoding to retain only linguistically valid diacritized sequences. By explicitly leveraging acoustic information and drastically reducing the decoding search space through these constraints, the method achieves superior accuracy while maintaining computational efficiency. Evaluated on the ArVoice and ClArTTS benchmarks, the proposed approach significantly lowers diacritic error rates compared to existing multimodal baselines, demonstrating both effectiveness and practicality for real-world deployment.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Constrained CTC Decoding
Diacritic Restoration
Non-autoregressive Modeling
Lattice-based Decoding
Arabic Speech Processing
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