AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| author | Wanzare, Lilian Amol, Cynthia Maina, zekiel Odhiambo, Nelson Kerubo, Hope Misula, Leila Oloo, Vivian Mboya, Rennish Onkoba, Edwin Ombui, Edward Muguro, Joseph Maina, Ciira wa Kipkebut, Andrew Otom, Alfred Omondi Kang'ethe, Ian Ndung'u Kanyi, Angela Wambui Omwenga, Brian Gichana |
| author_facet | Wanzare, Lilian Amol, Cynthia Maina, zekiel Odhiambo, Nelson Kerubo, Hope Misula, Leila Oloo, Vivian Mboya, Rennish Onkoba, Edwin Ombui, Edward Muguro, Joseph Maina, Ciira wa Kipkebut, Andrew Otom, Alfred Omondi Kang'ethe, Ian Ndung'u Kanyi, Angela Wambui Omwenga, Brian Gichana |
| contents | AfriVoices-KE is a large-scale multilingual speech dataset comprising approximately 3,000 hours of audio across five Kenyan languages: Dholuo, Kikuyu, Kalenjin, Maasai, and Somali. The dataset includes 750 hours of scripted speech and 2,250 hours of spontaneous speech, collected from 4,777 native speakers across diverse regions and demographics. This work addresses the critical underrepresentation of African languages in speech technology by providing a high-quality, linguistically diverse resource. Data collection followed a dual methodology: scripted recordings drew from compiled text corpora, translations, and domain-specific generated sentences spanning eleven domains relevant to the Kenyan context, while unscripted speech was elicited through textual and image prompts to capture natural linguistic variation and dialectal nuances. A customized mobile application enabled contributors to record using smartphones. Quality assurance operated at multiple layers, encompassing automated signal-to-noise ratio validation prior to recording and human review for content accuracy. Though the project encountered challenges common to low-resource settings, including unreliable infrastructure, device compatibility issues, and community trust barriers, these were mitigated through local mobilizers, stakeholder partnerships, and adaptive training protocols. AfriVoices-KE provides a foundational resource for developing inclusive automatic speech recognition and text-to-speech systems, while advancing the digital preservation of Kenya's linguistic heritage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08448 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages Wanzare, Lilian Amol, Cynthia Maina, zekiel Odhiambo, Nelson Kerubo, Hope Misula, Leila Oloo, Vivian Mboya, Rennish Onkoba, Edwin Ombui, Edward Muguro, Joseph Maina, Ciira wa Kipkebut, Andrew Otom, Alfred Omondi Kang'ethe, Ian Ndung'u Kanyi, Angela Wambui Omwenga, Brian Gichana Computation and Language AfriVoices-KE is a large-scale multilingual speech dataset comprising approximately 3,000 hours of audio across five Kenyan languages: Dholuo, Kikuyu, Kalenjin, Maasai, and Somali. The dataset includes 750 hours of scripted speech and 2,250 hours of spontaneous speech, collected from 4,777 native speakers across diverse regions and demographics. This work addresses the critical underrepresentation of African languages in speech technology by providing a high-quality, linguistically diverse resource. Data collection followed a dual methodology: scripted recordings drew from compiled text corpora, translations, and domain-specific generated sentences spanning eleven domains relevant to the Kenyan context, while unscripted speech was elicited through textual and image prompts to capture natural linguistic variation and dialectal nuances. A customized mobile application enabled contributors to record using smartphones. Quality assurance operated at multiple layers, encompassing automated signal-to-noise ratio validation prior to recording and human review for content accuracy. Though the project encountered challenges common to low-resource settings, including unreliable infrastructure, device compatibility issues, and community trust barriers, these were mitigated through local mobilizers, stakeholder partnerships, and adaptive training protocols. AfriVoices-KE provides a foundational resource for developing inclusive automatic speech recognition and text-to-speech systems, while advancing the digital preservation of Kenya's linguistic heritage. |
| title | AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.08448 |