Indigenous African languages are categorized as under-served in Artificial
Intelligence and suffer poor digital inclusivity and information access. The
challenge has been how to use machine learning and deep learning models without
the requisite data. Kencorpus is a Kenyan Language corpus that intends to
bridge the gap on how to collect, and store text and speech data that is good
enough to enable data-driven solutions in applications such as machine
translation, question answering and transcription in multilingual communities.
Kencorpus is a corpus (text and speech) for three languages predominantly
spoken in Kenya: Swahili, Dholuo and Luhya (dialects Lumarachi, Lulogooli and
Lubukusu). This corpus intends to fill the gap of developing a dataset that can
be used for Natural Language Processing and Machine Learning tasks for
low-resource languages. Each of these languages contributed text and speech
data for the language corpus. Data collection was done by researchers from
communities, schools and collaborating partners (media, publishers). Kencorpus
has a collection of 5,594 items, being 4,442 texts (5.6million words) and 1,152
speech files (177hrs). Based on this data, other datasets were also developed
e.g POS tagging sets for Dholuo and Luhya (50,000 and 93,000 words tagged
respectively), Question-Answer pairs from Swahili texts (7,537 QA pairs) and
Translation of texts into Swahili (12,400 sentences). The datasets are useful
for machine learning tasks such as text processing, annotation and translation.
The project also undertook proof of concept systems in speech to text and
machine learning for QA task, with initial results confirming the usability of
the Kencorpus to the machine learning community. Kencorpus is the first such
corpus of its kind for these low resource languages and forms a basis of
learning and sharing experiences for similar works.

Kencorpus, the first corpus of its kind for low-resource Indigenous African languages, endeavors to fill the gap in the development of Natural Language Processing and Machine Learning datasets for Swahili, Dholuo, and Luhya languages, enabling text and speech data-driven solutions in applications like machine translation, question-answering, and transcription.