{"id":14417,"date":"2026-10-02T06:00:42","date_gmt":"2026-10-02T06:00:42","guid":{"rendered":"https:\/\/krikis.lt\/nvidia-nemotron-optimizavimas-saudo-arabijos-dialektams\/"},"modified":"2026-10-02T06:00:42","modified_gmt":"2026-10-02T06:00:42","slug":"nvidia-nemotron-optimizavimas-saudo-arabijos-dialektams","status":"publish","type":"post","link":"https:\/\/krikis.lt\/en\/nvidia-nemotron-optimization-for-saudi-arabia-dialects\/","title":{"rendered":"NVIDIA Nemotron Optimization for Saudi Arabian Dialects \u2013 A Path to Other Language Variants"},"content":{"rendered":"

NVIDIA Nemotron 3.5 ASR (Automatic Speech Recognition) model is out of the box capable of transcribing up to 40 language locales, including Arabic, but in real-world deployments, it is often encountered with regional dialects and local recordings that are not adequately represented in the initial training data. This problem is particularly pronounced in Saudi Arabia, where both Modern Standard Arabic and specific dialects such as Najdi and Hijazi are spoken. In this article, we review how NVIDIA used the NeMo platform to perform precise fine-tuning to improve the model\u2019s performance in these dialects, and what conclusions can be applied to other language groups.<\/p>\n

Why is dialect adaptation necessary?<\/h2>\n
\"Human<\/figure>\n

An automatic speech recognition system must recognize not only standard speech, but also how people actually speak. Regional dialects and local recording conditions are often underrepresented in the training data of large, multilingual models. As a result, a model, while performing well on broad benchmarks, may not be able to accurately transcribe local accents or specific phonetic features. In the case of Saudi Arabia, this means that a model may be able to recognize Modern Standard Arabic or English well, but has difficulty recognizing Najdi and Hijazi.<\/p>\n

Fundamentals of the fine-tuning process<\/h2>\n
\"Computer<\/figure>\n

The NVIDIA team chose a \u201elow-resource\u201c corpus, a limited but carefully prepared dataset containing 103,559 out of 125,490 recordings, totaling 133.7 hours (\u2248 82.5 of the % original set). This filtering was not intended to remove hard-to-understand accents, but only structural flaws, such as poorly linked audio and text fragments. With the help of automatic quality assessments (UTMOS, SIGMOS), corpus-specific thresholds were set: UTMOS \u2265 1.25, SIGMOS noise \u2265 1.5, and overall SIGMOS \u2265 1.5, which allowed us to retain about 85 %-eligible recordings.<\/p>\n

After data preparation, the model was adapted using the NVIDIA NeMo framework and the ASR fine-tuning recipe. The key steps were:<\/p>\n