@inproceedings{wanzare-etal-2026-afrivoices,
title = "{A}fri{V}oices-{KE}: A Multilingual Speech Dataset for Kenyan Languages",
author = "Wanzare, Lilian and
Amol, Cynthia Jayne and
Maina, Ezekiel and
Odhiambo, Nelson and
Kerubo, Hope and
Misula, Leila and
Oloo, Vivian and
Mboya, Rennish and
Onkoba, Edwin and
Ombui, Edward and
Muguro, Joseph and
wa Maina, Ciira and
Kipkebut, Andrew and
Otom, Alfred Omondi and
Kang{'}ethe, Ian Ndung{'}u and
Kanyi, Angela Wambui and
Omwenga, Brian Gichana",
editor = "Ojha, Atul Kr. and
Sakti, Sakriani and
Soria, Claudia and
Melero, Maite and
McCrae, John P. and
Lignos, Constantine and
Liu, Chao-Hong and
Claramunt, German Rigau and
Rehm, Georg",
booktitle = "Proceedings of the {SIGUL} 2026 Joint Workshop with {ELE}, {EURALI}, and {DCLRL}: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages",
month = may,
year = "2026",
address = "Palma, Mallorca, Spain",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.sigul-1.29/",
doi = "10.63317/52g6nyparchg",
pages = "288--298",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages
%A Wanzare, Lilian
%A Amol, Cynthia Jayne
%A Maina, Ezekiel
%A Odhiambo, Nelson
%A Kerubo, Hope
%A Misula, Leila
%A Oloo, Vivian
%A Mboya, Rennish
%A Onkoba, Edwin
%A Ombui, Edward
%A Muguro, Joseph
%A wa Maina, Ciira
%A Kipkebut, Andrew
%A Otom, Alfred Omondi
%A Kang’ethe, Ian Ndung’u
%A Kanyi, Angela Wambui
%A Omwenga, Brian Gichana
%Y Ojha, Atul Kr.
%Y Sakti, Sakriani
%Y Soria, Claudia
%Y Melero, Maite
%Y McCrae, John P.
%Y Lignos, Constantine
%Y Liu, Chao-Hong
%Y Claramunt, German Rigau
%Y Rehm, Georg
%S Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca, Spain
%F wanzare-etal-2026-afrivoices
%X 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.
%R 10.63317/52g6nyparchg
%U https://aclanthology.org/2026.sigul-1.29/
%U https://doi.org/10.63317/52g6nyparchg
%P 288-298
Markdown (Informal)
[AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages](https://aclanthology.org/2026.sigul-1.29/) (Wanzare et al., SIGUL-EURALI-DCLRL 2026)
ACL
- Lilian Wanzare, Cynthia Jayne Amol, Ezekiel Maina, Nelson Odhiambo, Hope Kerubo, Leila Misula, Vivian Oloo, Rennish Mboya, Edwin Onkoba, Edward Ombui, Joseph Muguro, Ciira wa Maina, Andrew Kipkebut, Alfred Omondi Otom, Ian Ndung’u Kang’ethe, Angela Wambui Kanyi, and Brian Gichana Omwenga. 2026. AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages. In Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages, pages 288–298, Palma, Mallorca, Spain. ELRA Language Resources Association (ELRA).