Abstract
ASR has achieved remarkable global progress, yet African low-resource languages remain rigorously underrepresented, producing barriers to digital inclusion across the continent with more than +2000 languages. This systematic literature review (SLR) explores research on ASR for African languages with a focus on datasets, models and training methods, evaluation techniques, challenges, and recommends future directions. We employ the PRISMA 2020 procedures and search DBLP, ACM Digital Library, Google Scholar, Semantic Scholar, and arXiv for studies published between January 2020 and July 2025. We include studies related to ASR datasets, models or metrics for African languages, while excluding non-African, duplicates, and low-quality studies (score <3/5). We screen 71 out of 2,062 records and we record a total of 74 datasets across 111 languages, encompassing approximately 11,206 hours of speech. Fewer than 15% of research provided reproducible materials, and dataset licensing is not clear. Self-supervised and transfer learning techniques are promising, but are hindered by limited pre-training data, inadequate coverage of dialects, and the availability of resources. Most of the researchers use Word Error Rate (WER), with very minimal use of linguistically informed scores such as Character Error Rate (CER) or Diacritic Error Rate (DER), and thus with limited application in tonal and morphologically rich languages. The existing evidence on ASR systems is inconsistent, hindered by issues like dataset availability, poor annotations, licensing uncertainties, and limited benchmarking. Nevertheless, the rise of community-driven initiatives and methodological advancements indicates a pathway for improvement. Sustainable development for this area will also include stakeholder partnership, creation of ethically well-balanced datasets, use of lightweight modelling techniques, and active benchmarking.
Abstract (translated)
语音识别（ASR）在全球取得了显著进步，然而非洲低资源语言的代表性仍然严重不足，在拥有超过2000种语言的大洲上造成了数字包容性障碍。这项系统文献回顾（SLR）旨在探讨针对非洲语言的ASR研究，并重点关注数据集、模型和训练方法、评估技术、挑战以及未来方向的建议。我们采用PRISMA 2020程序，在DBLP、ACM Digital Library、Google Scholar、Semantic Scholar 和arXiv上搜索了从2020年1月至2025年7月间发表的研究。我们包括与非洲语言相关的ASR数据集、模型或指标的研究，同时排除非非洲的、重复的和低质量（评分<3/5）的研究。在2,062条记录中筛选出71篇符合要求的文章，并记录了涵盖11种不同语言共计约11,206小时语音的74个数据集。 研究发现，不到15%的研究提供了可重复使用的材料，且数据集许可情况不明朗。自我监督和迁移学习技术显示出潜力，但受限于预训练数据量不足、方言覆盖率低以及资源缺乏的问题。大多数研究人员使用单词错误率（WER），而较少采用语言学相关的指标如字符错误率（CER）或音调符号错误率（DER），因此在语调丰富和形态复杂的语言中应用有限。现有证据表明，ASR系统的性能受到数据集可用性差、注释质量低、许可不确定性以及基准测试不足等问题的限制。 尽管如此，社区驱动的倡议和技术方法的进步为改善现状提供了途径。这一领域的可持续发展还需包括利益相关者合作、建立伦理平衡的数据集、使用轻量级建模技术以及积极进行基准测试。
URL
https://arxiv.org/abs/2510.01145
