We describe Abjad AI’s submission to KSAA-2026 Shared Task 2 on automatic diacritization of Arabic speech dictation. The task requires generating fully diacritized text given speech audio and an undiacritized transcript. Because text-only diacritization cannot resolve ambiguities that are recoverable from the acoustic signal, we propose conditioning a character-level encoder-only Transformer (CATT) (Alasmary et al., 2024) on speech representations. We introduce grouped speech conditioning, which downsamples speech encoder features into a small set of pooled tokens concatenated to the text input, enabling efficient fusion without architectural changes to CATT. We train with a two-phase schedule that first freezes the text encoder, then fine-tunes the full model. Our best system, using Whisper-small (Rad-ford et al., 2022) features with five grouped tokens, achieves a Diacritization Error Rate (DER) of 6.60 and a Word Error Rate (WER) of 18.66 (without case endings, including no-diacritic) on the official test set. Notably, we find that Whisper-small consistently outperforms Whisper-large-v3, suggesting that compact speech representations better suit this fusion setting. This is an extended and revised version of our previous work (Ghannam et al., 2025).

We describe our submission to three NADI 2026 subtasks: Tunisian Arabic–French–English code-switched ASR (Task 1.3), dialectal Arabic speech translation, and 20-way Arabic dialect identification (ADI). For code-switched ASR, we finetune a 2B-parameter FastConformer encoder-decoder with synthetic Tunisian speech, cross-lingual data, waveform augmentation, and checkpoint averaging, achieving 14.12% WER and ranking first overall. For speech translation, finetuning Whisper Large-v3 yields strong in-domain results but poor generalization (BLEU 5.8 on blind test), illustrating overfitting under limited data. For ADI, a classifier on frozen pretrained ASR representations outperforms full/partial finetuning, reaching 42% accuracy across 20 dialects. We highlight synthetic augmentation, checkpoint averaging, and encoder freezing as effective low-resource strategies.

This paper describes the Abjad AI team’s sub- mission to the AraSeg 2026 Shared Task on Arabic sentence segmentation, covering the two punctuated tracks: Paragraph-Aware (PA) and No-Paragraph (NP). Rather than substituting a larger encoder, we retain the CAMeLBERT- MSA reference system and ask how much head- room remains in the task’s formulation. Our system combines four components: boundary labels aligned to each word’s final sub-token, an auxiliary classification head on the penul- timate encoder layer trained jointly with the final-layer head, class-weighted cross-entropy for boundary sparsity, and overlap-averaged decoding that scores documents of arbitrary length without truncation. We ablate each com- ponent in isolation, including the two that the reference system already uses. Our solution achieves F1 scores of 91.7 and 89.7 in the blind tests of the PA and NP sub-tasks, respectively.