Arabic ASR on the SADA Large-Scale Arabic Speech Corpus with Transformer-Based Models

• URL: http://arxiv.org/abs/2508.12968v1
• Date: Mon, 18 Aug 2025 14:44:25 GMT
• Title: Arabic ASR on the SADA Large-Scale Arabic Speech Corpus with Transformer-Based Models
• Authors: Branislav Gerazov, Marcello Politi, Sébastien Bratières,
• Abstract summary: We evaluate the performance of several automatic speech recognition models on a large-scale Arabic speech dataset.<n>The dataset contains 668 hours of high-quality audio from Saudi television shows.<n>We find that the MMS 1B model finetuned on SADA with a 4-gram language model achieves a WER of 40.9% and a CER of 17.6% on the SADA test clean set.
• Score: 3.2669219874106608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore the performance of several state-of-the-art automatic speech recognition (ASR) models on a large-scale Arabic speech dataset, the SADA (Saudi Audio Dataset for Arabic), which contains 668 hours of high-quality audio from Saudi television shows. The dataset includes multiple dialects and environments, specifically a noisy subset that makes it particularly challenging for ASR. We evaluate the performance of the models on the SADA test set, and we explore the impact of fine-tuning, language models, as well as noise and denoising on their performance. We find that the best performing model is the MMS 1B model finetuned on SADA with a 4-gram language model that achieves a WER of 40.9\% and a CER of 17.6\% on the SADA test clean set.

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