# Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2510.01145 · HTML: https://mlsanity.com/paper/2510.01145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 89 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 24 of 24)

- [AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR](https://mlsanity.com/paper/2310.00274/) (2023)
- [BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus](https://mlsanity.com/paper/2207.03546/) (2022)
- [A Material Lens on Coloniality in NLP](https://mlsanity.com/paper/2311.08391/) (2023)
- [AfriHuBERT: A self-supervised speech representation model for African languages](https://mlsanity.com/paper/2409.20201/) (2024)
- [Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users](https://mlsanity.com/paper/2104.13083/) (2021)
- [BembaSpeech: A Speech Recognition Corpus for the Bemba Language](https://mlsanity.com/paper/2102.04889/) (2021)
- [OkwuGb'e: End-to-End Speech Recognition for Fon and Igbo](https://mlsanity.com/paper/2103.07762/) (2021)
- [Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://mlsanity.com/paper/2502.03945/) (2025)
- [Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context](https://mlsanity.com/paper/2404.02000/) (2024)
- [AfriNames: Most ASR models "butcher" African Names](https://mlsanity.com/paper/2306.00253/) (2023)
- [`Ir`oy`inSpeech: A multi-purpose Yor`ub'a Speech Corpus](https://mlsanity.com/paper/2307.16071/) (2023)
- [Performant ASR Models for Medical Entities in Accented Speech](https://mlsanity.com/paper/2406.12387/) (2024)
- [Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect](https://mlsanity.com/paper/2407.04533/) (2024)
- [Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili](https://mlsanity.com/paper/2306.00410/) (2023)
- [AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages](https://mlsanity.com/paper/2303.12582/) (2023)
- [Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives](https://mlsanity.com/paper/2501.06478/) (2025)
- [Voice of a Continent: Mapping Africa's Speech Technology Frontier](https://mlsanity.com/paper/2505.18436/) (2025)
- [Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training](https://mlsanity.com/paper/1907.03064/) (2019)
- [Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages](https://mlsanity.com/paper/2011.03118/) (2020)
- [Fast Development of ASR in African Languages using Self Supervised Speech Representation Learning](https://mlsanity.com/paper/2103.08993/) (2021)
- [Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili](https://mlsanity.com/paper/2210.16537/) (2022)
- [Multilingual training set selection for ASR in under-resourced Malian languages](https://mlsanity.com/paper/2108.06164/) (2021)
- [Automatic Speech Recognition for the Ika Language](https://mlsanity.com/paper/2410.00940/) (2024)
- [A Deep Learning Automatic Speech Recognition Model for Shona Language](https://mlsanity.com/paper/2507.21331/) (2025)

## Built on by (most cited first, 2 of 2)

- [From Speech to Text Corpora: Evaluating ASR-Based Data Acquisition for Low-Resource Fongbe and Hausa](https://mlsanity.com/paper/2606.22274/) (2026)
- [Building an ASR Solution for Training and Assessing Children's Reading](https://mlsanity.com/paper/2606.31508/) (2026)

## Abstract (arXiv)

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.