Classifying topics in speech when all you have is crummy translations
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Given a large amount of unannotated speech in a language with few resources, can we classify the speech utterances by topic? We show that this is possible if text translations are available for just a small amount of speech (less than 20 hours), using a recent model for direct speech-to-text translation. While the translations are poor, they are still good enough to correctly classify 1-minute speech segments over 70 majority-class baseline. Such a system might be useful for humanitarian applications like crisis response, where incoming speech must be quickly assessed for further action.
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