Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition

• URL: http://arxiv.org/abs/2601.06802v1
• Date: Sun, 11 Jan 2026 08:28:31 GMT
• Title: Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition
• Authors: Ayman Mansour,
• Abstract summary: This paper presents a study of data augmentation techniques for fine-tuning OpenAI Whisper models.<n>It establishes the first benchmark for the Sudanese dialect.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although many Automatic Speech Recognition (ASR) systems have been developed for Modern Standard Arabic (MSA) and Dialectal Arabic (DA), few studies have focused on dialect-specific implementations, particularly for low-resource Arabic dialects such as Sudanese. This paper presents a comprehensive study of data augmentation techniques for fine-tuning OpenAI Whisper models and establishes the first benchmark for the Sudanese dialect. Two augmentation strategies are investigated: (1) self-training with pseudo-labels generated from unlabeled speech, and (2) TTS-based augmentation using synthetic speech from the Klaam TTS system. The best-performing model, Whisper-Medium fine-tuned with combined self-training and TTS augmentation (28.4 hours), achieves a Word Error Rate (WER) of 57.1% on the evaluation set and 51.6% on an out-of-domain holdout set substantially outperforming zero-shot multilingual Whisper (78.8% WER) and MSA-specialized Arabic models (73.8-123% WER). All experiments used low-cost resources (Kaggle free tier and Lightning.ai trial), demonstrating that strategic data augmentation can overcome resource limitations for low-resource dialects and provide a practical roadmap for developing ASR systems for low-resource Arabic dialects and other marginalized language varieties. The models, evaluation benchmarks, and reproducible training pipelines are publicly released to facilitate future research on low-resource Arabic ASR.

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