@inproceedings{imam-etal-2025-automatic,
title = "Automatic Speech Recognition for {A}frican Low-Resource Languages: Challenges and Future Directions",
author = "Imam, Sukairaj Hafiz and
Sani, Babangida and
Gete, Dawit Ketema and
Ahmed, Bedru Yimam and
Ahmad, Ibrahim Said and
Abdulmumin, Idris and
Yimam, Seid Muhie and
Bello, Muhammad Yahuza and
Muhammad, Shamsuddeen Hassan",
editor = "Lignos, Constantine and
Abdulmumin, Idris and
Adelani, David",
booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.africanlp-1.13/",
doi = "10.18653/v1/2025.africanlp-1.13",
pages = "89--94",
ISBN = "979-8-89176-257-2",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions
%A Imam, Sukairaj Hafiz
%A Sani, Babangida
%A Gete, Dawit Ketema
%A Ahmed, Bedru Yimam
%A Ahmad, Ibrahim Said
%A Abdulmumin, Idris
%A Yimam, Seid Muhie
%A Bello, Muhammad Yahuza
%A Muhammad, Shamsuddeen Hassan
%Y Lignos, Constantine
%Y Abdulmumin, Idris
%Y Adelani, David
%S Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-257-2
%F imam-etal-2025-automatic
%X 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.
%R 10.18653/v1/2025.africanlp-1.13
%U https://aclanthology.org/2025.africanlp-1.13/
%U https://doi.org/10.18653/v1/2025.africanlp-1.13
%P 89-94
Markdown (Informal)
[Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions](https://aclanthology.org/2025.africanlp-1.13/) (Imam et al., AfricaNLP 2025)
ACL
- Sukairaj Hafiz Imam, Babangida Sani, Dawit Ketema Gete, Bedru Yimam Ahmed, Ibrahim Said Ahmad, Idris Abdulmumin, Seid Muhie Yimam, Muhammad Yahuza Bello, and Shamsuddeen Hassan Muhammad. 2025. Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions. In Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025), pages 89–94, Vienna, Austria. Association for Computational Linguistics.