  In societies with well developed internet infrastructure, social media is the
leading medium of communication for various social issues especially for
breaking news situations. In rural Uganda however, public community radio is
still a dominant means for news dissemination. Community radio gives audience
to the general public especially to individuals living in rural areas, and thus
plays an important role in giving a voice to those living in the broadcast
area. It is an avenue for participatory communication and a tool relevant in
both economic and social development.This is supported by the rise to ubiquity
of mobile phones providing access to phone-in or text-in talk shows. In this
paper, we describe an approach to analysing the readily available community
radio data with machine learning-based speech keyword spotting techniques. We
identify the keywords of interest related to agriculture and build models to
automatically identify these keywords from audio streams. Our contribution
through these techniques is a cost-efficient and effective way to monitor food
security concerns particularly in rural areas. Through keyword spotting and
radio talk show analysis, issues such as crop diseases, pests, drought and
famine can be captured and fed into an early warning system for stakeholders
and policy makers.
