# AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages

arXiv: https://arxiv.org/abs/2604.08448 · HTML: https://mlsanity.com/paper/2604.08448/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [XTREME-S: Evaluating Cross-lingual Speech Representations](https://mlsanity.com/paper/2203.10752/) (2022)
- [BembaSpeech: A Speech Recognition Corpus for the Bemba Language](https://mlsanity.com/paper/2102.04889/) (2021)
- [Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks](https://mlsanity.com/paper/2208.12081/) (2022)
- [`Ir`oy`inSpeech: A multi-purpose Yor`ub'a Speech Corpus](https://mlsanity.com/paper/2307.16071/) (2023)
- [State of NLP in Kenya: A Survey](https://mlsanity.com/paper/2410.09948/) (2024)
- [Building low-resource African language corpora: A case study of Kidawida, Kalenjin and Dholuo](https://mlsanity.com/paper/2501.11003/) (2025)

## Built on by (most cited first, 1 of 1)

- [From a Multilingual Streaming ASR Backbone to Kenyan-Language Systems: Data-Centric Adaptation of Nemotron 3.5 for Kikuyu, Dholuo, and Kalenjin](https://mlsanity.com/paper/2607.18912/) (2026)

## Abstract (arXiv)

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.