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.

翻译：自动语音识别（ASR）技术已深刻改变了人机交互方式；然而，非洲的低资源语言在研究和实际应用中仍存在显著代表性不足的问题。本研究探讨了阻碍这些语言ASR系统发展的主要挑战，包括数据稀缺性、语言复杂性、有限的计算资源、声学变异性以及围绕偏见与隐私的伦理问题。主要目标是批判性分析这些障碍，并确定在非洲语境下推进ASR技术的实用且包容的策略。近期进展与案例研究强调了若干前景广阔的路径，例如社区驱动的数据收集、自监督与多语言学习、轻量级模型架构以及注重隐私保护的技术。来自涉及多种非洲语言的试点项目证据表明，定制化解决方案具有可行性与影响力，这些方案涵盖基于语素的建模以及医疗、教育等领域的特定场景ASR应用。研究结果凸显了跨学科合作与持续投资对于应对非洲大陆所面临的特有语言和基础设施挑战的重要性。本研究为构建符合伦理、高效且包容的ASR系统提供了渐进式路线图，此类系统不仅能保护语言多样性，还能提升数字可及性并促进非洲语言使用者的社会经济参与。