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Thaka system wins Arabic speech diacritization task with fine-tuned CATT-Whisper

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a winning system for the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization. The system, named Thaka, fine-tunes a CATT-Whisper multimodal model using a limited dataset of 2,327 samples. Key to its success were training regularization techniques, including R-Drop consistency regularization, optimized hyperparameters, and Focal Loss, along with averaging 200 stochastic forward passes from four model checkpoints during inference. This approach resulted in a Word Error Rate (WER) of 23.26%, securing first place among participants. AI

IMPACT Demonstrates advanced fine-tuning techniques for low-resource speech diacritization tasks.

RANK_REASON The cluster contains a research paper detailing a winning system for a specific task in automatic speech recognition and diacritization.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Thaka system wins Arabic speech diacritization task with fine-tuned CATT-Whisper

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The cluster contains a research paper detailing a winning system for a specific task in automatic speech recognition and diacritization.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Meshal Alamr, Hassan Alqaeri, Abdullah Aldahlawi ·

    Thaka at KSAA-2026 Task 2: Regularized Fine-Tuning for Arabic Speech Diacritization

    arXiv:2605.25928v1 Announce Type: new Abstract: We describe the winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization. The task requires producing fully diacritized Arabic text from speech audio and undiacritized transcrip…

  2. arXiv cs.CL TIER_1 English(EN) · Abdullah Aldahlawi ·

    Thaka at KSAA-2026 Task 2: Regularized Fine-Tuning for Arabic Speech Diacritization

    We describe the winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization. The task requires producing fully diacritized Arabic text from speech audio and undiacritized transcripts, with only 2,327 training samples available a…

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