A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development
This survey systematically catalogs publicly available text and speech resources for Hausa (80-100 million speakers) and Fongbe (2 million speakers in Benin). Findings show Hausa has broader text resource diversity across news, encyclopedic, and educational domains, while Fongbe has seen recent academic speech data collection initiatives. Both languages are represented in Masakhane benchmarks for NER and POS tagging. The paper provides task-specific recommendations and identifies priority gaps including domain-diverse Fongbe text and dedicated Hausa speech corpora.
Article intelligence
Key points
- Hausa benefits from more diverse text resources; Fongbe has limited text but recent speech data collection.
- Both languages are included in Masakhane benchmarks for NER and POS tagging.
- Priority gaps identified: domain-diverse Fongbe text and dedicated Hausa speech corpora.
- Survey covers parallel corpora, monolingual texts, speech datasets, pre-trained models, and evaluation benchmarks.
Why it matters
This matters because hausa benefits from more diverse text resources; Fongbe has limited text but recent speech data collection.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.22828] A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development
[Submitted on 13 Apr 2026]
Title:A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development
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Abstract:This survey provides a comprehensive catalog of publicly available text and speech resources for two West African languages: Hausa, an Afroasiatic language with approximately 80-100 million speakers, and Fongbe, a Niger-Congo language spoken by approximately 2 million people in Benin. These languages represent contrasting cases on the resource availability spectrum. We address the question: \textit{What is the current state of publicly available NLP resources for Hausa and Fongbe, and what gaps remain?} Through systematic search of academic repositories, data platforms, and web sources, we catalog parallel corpora, monolingual text collections, speech datasets, pre-trained models, and evaluation benchmarks. For each resource, we document size, domain coverage, format, licensing, and accessibility. Our findings reveal that Hausa benefits from broader text resource diversity across news, encyclopedic, and educational domains. Fongbe, while having more limited text resources, has been the focus of recent academic speech data collection initiatives. Both languages are represented in Masakhane benchmarks for NER and POS tagging. We provide task-specific recommendations and identify priority gaps including domain-diverse Fongbe text and dedicated Hausa speech corpora.
Comments: 8 pages, 7 tables; survey paper; to appear in IEEE SDS 2026
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2.7; H.3.1; I.2.0
Cite as: arXiv:2605.22828 [cs.CL]
(or arXiv:2605.22828v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.22828
arXiv-issued DOI via DataCite
Submission history
From: Mahounan Pericles Adjovi [view email] [v1] Mon, 13 Apr 2026 10:59:44 UTC (91 KB)
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