A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development
Quick Take
This survey catalogs publicly available NLP resources for Hausa and Fongbe, revealing that Hausa has diverse resources across various domains, while Fongbe lacks text resources but has recent speech data initiatives. Key gaps include the need for more domain-diverse Fongbe texts and dedicated Hausa speech corpora.
Key Points
- Hausa has 80-100 million speakers; Fongbe has about 2 million.
- Diverse resources for Hausa include news, encyclopedic, and educational texts.
- Fongbe is focusing on recent academic speech data collection initiatives.
- Both languages are included in Masakhane benchmarks for NER and POS tagging.
- Identified gaps include the need for more Fongbe text and dedicated Hausa speech datasets.
Article Content
From source RSS / original summaryarXiv:2605. 22828v1 Announce Type: new 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.
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