  For many of the 700 million illiterate people around the world, speech
recognition technology could provide a bridge to valuable information and
services. Yet, those most in need of this technology are often the most
underserved by it. In many countries, illiterate people tend to speak only
low-resource languages, for which the datasets necessary for speech technology
development are scarce. In this paper, we investigate the effectiveness of
unsupervised speech representation learning on noisy radio broadcasting
archives, which are abundant even in low-resource languages. We make three core
contributions. First, we release two datasets to the research community. The
first, West African Radio Corpus, contains 142 hours of audio in more than 10
languages with a labeled validation subset. The second, West African Virtual
Assistant Speech Recognition Corpus, consists of 10K labeled audio clips in
four languages. Next, we share West African wav2vec, a speech encoder trained
on the noisy radio corpus, and compare it with the baseline Facebook speech
encoder trained on six times more data of higher quality. We show that West
African wav2vec performs similarly to the baseline on a multilingual speech
recognition task, and significantly outperforms the baseline on a West African
language identification task. Finally, we share the first-ever speech
recognition models for Maninka, Pular and Susu, languages spoken by a combined
10 million people in over seven countries, including six where the majority of
the adult population is illiterate. Our contributions offer a path forward for
ethical AI research to serve the needs of those most disadvantaged by the
digital divide.
