Building a usable radio monitoring automatic speech recognition (ASR) system
is a challenging task for under-resourced languages and yet this is paramount
in societies where radio is the main medium of public communication and
discussions. Initial efforts by the United Nations in Uganda have proved how
understanding the perceptions of rural people who are excluded from social
media is important in national planning. However, these efforts are being
challenged by the absence of transcribed speech datasets. In this paper, The
Makerere Artificial Intelligence research lab releases a Luganda radio speech
corpus of 155 hours. To our knowledge, this is the first publicly available
radio dataset in sub-Saharan Africa. The paper describes the development of the
voice corpus and presents baseline Luganda ASR performance results using Coqui
STT toolkit, an open source speech recognition toolkit.

本文介绍了一个基于 Coqui STT 开源工具包的 Luganda 语音语料库的开发，该语料库包含了 155 小时的无线电记录，是撒哈拉以南非洲首个公开无线电数据集，用于在没有社交媒体的社会中识别边缘人群的言语和观点。