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

Cross-lingual topic prediction for speech using translations