  Given a large amount of unannotated speech in a low-resource language, can we
classify the speech utterances by topic? We consider this question in the
setting where a small amount of speech in the low-resource language is paired
with text translations in a high-resource language. We develop an effective
cross-lingual topic classifier by training on just 20 hours of translated
speech, using a recent model for direct speech-to-text translation. While the
translations are poor, they are still good enough to correctly classify the
topic of 1-minute speech segments over 70% of the time - a 20% improvement over
a majority-class baseline. Such a system could be useful for humanitarian
applications like crisis response, where incoming speech in a foreign
low-resource language must be quickly assessed for further action.
