Scaling Pre-training Data Size and Domain-Specific Fine-Tuning in XLSR-53 for Low-Resource Perso-Arabic ASR

Abstract

Abstract: Although commercial Arabic automatic speech recognition (ASR) systems support Modern Standard Arabic (MSA), they struggle with dialectal speech. We investigate the effect of fine-tuning OpenAI's Whisper on five major Arabic dialects (Gulf, Levantine, Iraqi, Egyptian, Maghrebi) using Mozilla Common Voice for MSA and the MASC dataset for dialectal speech. We evaluate MSA training size effects, benefits of pre-training on MSA data, and dialect-specific versus dialect-pooled models. We find that small amounts of MSA fine-tuning data yield substantial improvements for smaller models, matching large

Research Question

What is the impact of scaling the pre-training data size on the WER of low-resource Perso-Arabic ASR when using XLSR-53 with domain-specific fine-tuning?

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