Automatic speech recognition (ASR) for African low-resource languages (LRLs) is often limited by scarce labelled data and the high cost of adapting large foundation models. This study evaluates whether parameter-efficient fine-tuning (PEFT) can serve as a practical alternative to full fine-tuning (FFT) for adapting Whisper-Small with limited labelled speech and constrained compute. We used a 10-hour subset of NaijaVoices covering Hausa, Yorùbá, and Igbo, and we compared FFT with several PEFT strategies under a fixed evaluation protocol. DoRA attains a 22.0% macro-average WER, closely aligning with the 22.1% achieved by FFT while updating only 4M parameters rather than 240M, and this difference remains within run-to-run variation across random seeds. Yorùbá consistently yields the lowest word error rates, whereas Igbo remains the most challenging, indicating that PEFT can deliver near FFT accuracy with substantially lower training and storage requirements for low-resource African ASR.

Automatic Speech Recognition (ASR) technologies have transformed human-computer interaction; however, low-resource languages in Africa remain significantly underrepresented in both research and practical applications. This study investigates the major challenges hindering the development of ASR systems for these languages, which include data scarcity, linguistic complexity, limited computational resources, acoustic variability, and ethical concerns surrounding bias and privacy. The primary goal is to critically analyze these barriers and identify practical, inclusive strategies to advance ASR technologies within the African context. Recent advances and case studies emphasize promising strategies such as community-driven data collection, self-supervised and multilingual learning, lightweight model architectures, and techniques that prioritize privacy. Evidence from pilot projects involving various African languages showcases the feasibility and impact of customized solutions, which encompass morpheme-based modeling and domain-specific ASR applications in sectors like healthcare and education. The findings highlight the importance of interdisciplinary collaboration and sustained investment to tackle the distinct linguistic and infrastructural challenges faced by the continent. This study offers a progressive roadmap for creating ethical, efficient, and inclusive ASR systems that not only safeguard linguistic diversity but also improve digital accessibility and promote socioeconomic participation for speakers of African languages.