Despite major advances in information retrieval (IR) and language technologies, speakers of extremely low-resource ethnic community languages (ECLs) remain largely excluded from digital information access. With Tanzania having over 120 languages, yet most IR systems and digital assistants primarily support English and, to a limited extent, Kiswahili. This disproportionately affects rural populations (over 65%), where ECLs are used alongside Kiswahili in daily life. This paper presents the ongoing Ethnic Voices initiative, which addresses this gap by building foundational text and speech resources explicitly relevant to IR research in extremely low-resource multilingual settings. The project targets six ECLs in Tanzania, with current data collection completed for Hehe and Luguru. Data collection from the two ECLs has produced over 800 Kiswahili-ECL parallel sentences per language and over 95 hours of speech. Preliminary survey results show strong demand for multilingual and voice-based access to information. It further identifies language barriers as a primary limitation of existing English-centric IR systems. This also paper discusses the IR implications of these findings and outlines directions toward inclusive retrieval and evaluation in extremely low-resource languages. Beyond resource creation, we position the collected dataset as IR-enabling infrastructure that supports the formulation of retrieval tasks, the development of baseline systems, and the design of evaluation protocols in benchmark-scarce, extremely low-resource multilingual settings.

Access to and consumption of agricultural research findings remains a challenge for Kiswahili-speaking farmers and extension officers in Tanzania due to the predominance of English in agriculture scholarly publications.To address this challenge, the Mkulima repository, a digital collection of over 600 Swahili agricultural publications, was developed at the Sokoine University of Agriculture to provide agricultural knowledge in Kiswahili.However, its current structure limits effective retrieval and accessibility, given the type of its intended audience, smallholder farmers.This work-in-progress aims to improve access to agricultural knowledge in Kiswahili through a hybrid model that integrates a domain-specific Knowledge Graph (KG) with Retrieval-Augmented Generation (RAG), an approach that combines traditional retrieval with generative language models for producing informed answers.The project's findings aim to contribute to AI-driven retrieval systems for low-resource languages, with results targeted for submission as a paper to SIGIR 2026.