arXiv — NLP / Computation & Language · · 3 min read

A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development

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Computer Science > Computation and Language

arXiv:2605.22828 (cs)
[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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