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Addressing Accent Disparities in Automatic Speech Recognition: A Comparative Study of Single and Two-Step Adaptation

Automatic speech recognition (ASR) systems often exhibit uneven performance across accents, raising concerns about fairness and bias. This study investigates the impact of model fine-tuning strategies on ASR performance and accent-related disparities. We conduct a controlled empirical evaluation of two adaptation approaches—single-step and two-step fine-tuning—using pretrained Whisper (small) and Wav2Vec2-XLSR-53 models on African-accented English speech from the AfriSpeech-200 dataset, covering Yoruba, Igbo, Swahili, and Hausa accents. Both fine-tuning strategies substantially reduced mean word error rate (WER) for all models. However, these improvements did not translate into consistent reductions in accent-related performance gaps. When analysed separately across general and clinical subsets, WER gaps often increased due to uneven gains across accents. Although two-step fine-tuning provided modest improvements over single-step adaptation, its impact on reducing disparities remained limited. These findings indicate that fine-tuning primarily optimises performance without effectively addressing systematic bias across speaker groups, even when models are specialised for individual accents. This highlights the limitations of per-accent specialisation as a practical bias mitigation strategy.