Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank
Audio datasets are fundamental to the development of automatic speech-recognition (ASR) systems. However, the availability of a large corpus of audio datasets in low-resource languages (LRLs) is limited. This study addresses this gap by introducing audio speech datasets for five low-resource languag...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925006043 |
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| author | Isaac Wiafe Jamal-Deen Abdulai Akon Obu Ekpezu Raynard Dodzi Helegah Elikem Doe Atsakpo Charles Nutrokpor Fiifi Baffoe Payin Winful Kafui Kwashie Solaga |
| author_facet | Isaac Wiafe Jamal-Deen Abdulai Akon Obu Ekpezu Raynard Dodzi Helegah Elikem Doe Atsakpo Charles Nutrokpor Fiifi Baffoe Payin Winful Kafui Kwashie Solaga |
| author_sort | Isaac Wiafe |
| collection | DOAJ |
| description | Audio datasets are fundamental to the development of automatic speech-recognition (ASR) systems. However, the availability of a large corpus of audio datasets in low-resource languages (LRLs) is limited. This study addresses this gap by introducing audio speech datasets for five low-resource languages spoken in Ghana and parts of Togo. Specifically, it presents a 5000-hour speech corpus in Akan, Ewe, Dagbani, Dagaare, and Ikposo. Each language corpus includes 1000 h of validated audio speech recorded by their indigenous speakers. These audio recordings are spoken descriptions of 1000 culturally relevant images collected using a custom Android mobile application. To enhance the dataset’s utility in ASR and linguistic research 10 % of the audio recordings for each language were randomly selected and transcribed, resulting in approximately 100 h of transcription per language. This dataset represents a critical resource for preserving and documenting Ghanaian languages. It holds the potential for advancing speech and language technologies in these languages. Creating this audio dataset is the first step towards bridging the technological gap between high- and low-resource languages. Ethical guidelines were strictly followed throughout the data collection process and participants were given incentives for lending their voices to this study. |
| format | Article |
| id | doaj-art-07719d37eaea485dba01b925dcec26c4 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-07719d37eaea485dba01b925dcec26c42025-08-20T03:57:36ZengElsevierData in Brief2352-34092025-08-016111188010.1016/j.dib.2025.111880Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data BankIsaac Wiafe0Jamal-Deen Abdulai1Akon Obu Ekpezu2Raynard Dodzi Helegah3Elikem Doe Atsakpo4Charles Nutrokpor5Fiifi Baffoe Payin Winful6Kafui Kwashie Solaga7Corresponding author.; Department of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaDepartment of Computer Science, University of Ghana, Legon-Accra, GhanaAudio datasets are fundamental to the development of automatic speech-recognition (ASR) systems. However, the availability of a large corpus of audio datasets in low-resource languages (LRLs) is limited. This study addresses this gap by introducing audio speech datasets for five low-resource languages spoken in Ghana and parts of Togo. Specifically, it presents a 5000-hour speech corpus in Akan, Ewe, Dagbani, Dagaare, and Ikposo. Each language corpus includes 1000 h of validated audio speech recorded by their indigenous speakers. These audio recordings are spoken descriptions of 1000 culturally relevant images collected using a custom Android mobile application. To enhance the dataset’s utility in ASR and linguistic research 10 % of the audio recordings for each language were randomly selected and transcribed, resulting in approximately 100 h of transcription per language. This dataset represents a critical resource for preserving and documenting Ghanaian languages. It holds the potential for advancing speech and language technologies in these languages. Creating this audio dataset is the first step towards bridging the technological gap between high- and low-resource languages. Ethical guidelines were strictly followed throughout the data collection process and participants were given incentives for lending their voices to this study.http://www.sciencedirect.com/science/article/pii/S2352340925006043Speech-to-textSpeech synthesisLow-resource languagesNatural language processingText-to-speechSpeech datasets |
| spellingShingle | Isaac Wiafe Jamal-Deen Abdulai Akon Obu Ekpezu Raynard Dodzi Helegah Elikem Doe Atsakpo Charles Nutrokpor Fiifi Baffoe Payin Winful Kafui Kwashie Solaga Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank Data in Brief Speech-to-text Speech synthesis Low-resource languages Natural language processing Text-to-speech Speech datasets |
| title | Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank |
| title_full | Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank |
| title_fullStr | Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank |
| title_full_unstemmed | Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank |
| title_short | Advancing automatic speech recognition for low-resource ghanaian languages: Audio datasets for Akan, Ewe, Dagbani, Dagaare, and IkposoScience Data Bank |
| title_sort | advancing automatic speech recognition for low resource ghanaian languages audio datasets for akan ewe dagbani dagaare and ikpososcience data bank |
| topic | Speech-to-text Speech synthesis Low-resource languages Natural language processing Text-to-speech Speech datasets |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925006043 |
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