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Itgan adapts Whisper for robust Arabic ASR at NADI 2026

By PulseAugur Editorial · [1 sources] ·

Researchers from Itgan have detailed their systems for the NADI 2026 shared task, focusing on Arabic Automatic Speech Recognition (ASR) across robust, mixed-dialect, and code-switched scenarios. Their approach primarily utilized Whisper, adapted with Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA, and trained on consumer GPUs. The systems achieved competitive results, including a 14.49% word error rate on Tunisian code-switched ASR and a 5.38% character error rate, with further improvements gained through model averaging and ROVER voting. AI

IMPACT Presents novel adaptations of Whisper for challenging Arabic speech recognition tasks.

RANK_REASON The cluster describes a research paper detailing systems for an academic shared task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Itgan adapts Whisper for robust Arabic ASR at NADI 2026

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The cluster describes a research paper detailing systems for an academic shared task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ibrahim Almajai ·

    Itgan at NADI 2026 shared task: Parameter-Efficient Whisper Adaptation for Robust, Mixed-Dialect and Code-Switched Arabic ASR

    arXiv:2610.09934v1 Announce Type: new Abstract: We describe the Itgan systems for the three ASR subtasks of NADI 2026, namely robust country-level ASR (1.1), mixed-dialect ASR (1.2), and Tunisian code-switched ASR (1.3). All three share one recipe, Whisper adapted with LoRA on co…

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