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 transcripts, with only 2,327 training samples available and no external data permitted. Our system fine-tunes CATT-Whisper, a character-level multimodal model combining a pretrained CATT text encoder with a frozen Whisper speech encoder. The key to our approach is training regularization: R-Drop consistency regularization, Optu
The continuous advancements in AI and natural language processing highlight an ongoing push towards more robust and culturally specific AI applications.
This development indicates progress in making sophisticated AI accessible and effective for less-resourced languages, expanding the global reach and utility of AI systems.
The ability to accurately diacritize Arabic speech with limited data signifies a practical step towards overcoming data scarcity challenges for specific linguistic AI tasks.
- · Arabic-speaking populations
- · NLP researchers
- · AI model developers
- · Speech-to-text providers
- · Developers of general-purpose, non-specific AI models
- · Manual diacritization services
Improved accuracy and efficiency for Arabic speech-to-text and language processing applications.
Increased adoption of AI tools within Arabic-speaking professional and consumer markets due to better linguistic specificity.
Potential for development of similar low-resource language specific models for other complex languages, fostering a more linguistically diverse AI landscape.
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Read at arXiv cs.CL