@inproceedings{esmaeil-etal-2026-tantaarabnlp,
    title = "{T}anta{A}rab{NLP} at {KSAA}-2026 Task 2: Adapting {CATT}-Whisper for {A}rabic Speech Dictation with Automatic Diacritization",
    author = "Esmaeil, Nada Adel  and
      Elbasiony, Reda M.  and
      Faheem, Mohamed T.",
    editor = "Al-Khalifa, Hend  and
      El-Haj, Mo  and
      Ezzini, Saad",
    booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-workflow/2026.osact-1.30/",
    doi = "10.63317/46cm97fcekow",
    pages = "229--233",
    abstract = "We present our submission to the KSAA-2026 Shared Task (Subtask 2): Automatic Diacritization of Speech Dictation. Building upon the CATT-Whisper multimodal architecture, which fuses representations from a pre-trained CATT text encoder and the Whisper speech encoder, we fine-tune the model end-to-end on the official shared task training data. To further enhance performance on speech-dictated Arabic text, we apply careful post-processing to the model outputs. Our best submission achieves a Diacritic Error Rate (DER) of 7.04, a Word Error Rate (WER) of 24.39, and a Sentence Error Rate (SER) of 71.65 on the hidden test set, securing 2nd place in the competition. These results demonstrate the effectiveness of adapting a strong multimodal baseline to the speech-aware diacritization setting and highlight the value of task-specific fine-tuning and output refinement for bridging the gap between spoken transcripts and fully diacritized Arabic text."
}