With recent advancements in speech recognition, it is crucial to ensure that automatic speech recognition (ASR) systems do not exhibit systematic biases, such as those related to gender, age, accent, and dialect. Although research has extensively examined systematic biases such as those related to gender, age, accent, and dialect, for high-resource languages, research on low-resource African languages remains limited. This systematic literature review synthesizes evidence on bias evaluation and mitigation in ASR models for African languages, adhering to the PRISMA reporting guidelines. Our analysis reveals that most biases stem from data imbalances and limited linguistic diversity in training datasets, resulting in disproportionately high error rates for underrepresented speaker groups. Mitigation strategies in African contexts have primarily focused on data-centric methods, including dataset expansion, augmentation, and transfer learning. In contrast, more advanced approaches, including fairness-aware modeling, bias-aware loss functions, adversarial debiasing, and speaker-adaptive techniques, are rarely applied. Gender, accent, and dialect biases dominate the few African studies available, while age and racial biases are almost absent. The limited number of African languages covered highlights the urgent need for more representative and inclusive research. Addressing these gaps will support the development of fairer and more robust ASR technologies across the continent.
Nakatumba‐Nabende et al. (Sat,) studied this question.