Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv preprint arXiv
Abstract
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.
Description
Keywords
Keyword Spotter Model, Crop Pest and Disease Monitoring, Community Radio Data
Citation
Akera, B., Nakatumba-Nabende, J., Mukiibi, J., Hussein, A., Baleeta, N., Ssendiwala, D., & Nalwooga, S. (2019). Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data. arXiv preprint arXiv:1910.02292.