Evidence map›Paper›PMID 42569639›Full record

ArticleData in brief2026

A parallel English-Akan maternal health dataset to support machine translation and text-to-speech systems.

Isaac Wiafe, Akon Obu Ekpezu, Fiifi Baffoe Payin Winful, Akosua Nyarkoa Wiafe-Akenten, Mark Atta Mensah, Nissi Semanyoh, Justice Kwame Appati, Sumaya Ahmed Salihs, Evans Kwasi, Gifty Odame and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Isaac WiafeDepartment of Computer Science, University of Ghana, Ghana.
Akon Obu EkpezuDepartment of Computer Science, University of Ghana, Ghana.
Fiifi Baffoe Payin WinfulDepartment of Computer Science, University of Ghana, Ghana.
Akosua Nyarkoa Wiafe-AkentenDepartment of Computer Science, University of Ghana, Ghana.
Mark Atta MensahDepartment of Computer Science, University of Ghana, Ghana.
Nissi SemanyohDepartment of Computer Science, University of Ghana, Ghana.
Justice Kwame AppatiDepartment of Computer Science, University of Ghana, Ghana.
Sumaya Ahmed SalihsDepartment of Computer Science, University of Ghana, Ghana.
Evans KwasiDepartment of Computer Science, University of Ghana, Ghana.
Gifty OdameUniversity Health Services, University of Ghana, Ghana.
Kelvin Nketia-AchiampongDepartment of Computer Science, University of Ghana, Ghana.
Kwaku Owusu OseiDepartment of Computer Science, University of Ghana, Ghana.
Frank Ernest YeboahDepartment of Computer Science, University of Ghana, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Audio and text datasets are essential for developing machine translation, text-to-speech systems, and speech-enabled systems. However, domain-specific datasets for low-resource African languages remain limited, particularly in the healthcare domain. This study addresses this gap by introducing a parallel English-Akan maternal health dataset to support machine translation and text-to-speech systems. The dataset consists of 3487 unique English maternal health phrases (question-and-answer) generated from verified digital health sources and medically reviewed for Ghanaian contextual relevance. These phrases were translated into Akan by four linguistic experts, producing 12,000 Akan transcriptions and 12,000 corresponding Akan audio recordings, totalling 32.313 h. The dataset covers prenatal and postnatal maternal health themes that are aligned with the WHO-recommended domains. The audio recordings were captured in soundproof vocal booths and processed into .wav format. This dataset provides a valuable resource for advancing Akan health communication technologies, maternal health chatbots, and low-resource language AI research.

Indexed as

AkanLow-resource languagesMachine translationMaternal healthNatural language processingParallel corpusPre- and post-natal careText-to-speech

Identifiers

PMID42569639
PMCPMC13450209

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.