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We consider hate speech detection through keyword spotting on radiobroadcasts. One approach is to build an automatic speech recognition (ASR)system for the target low-resource language. We compare this to using acousticword embedding (AWE) models that map speech segments to a space where matchingwords have similar vectors. We specifically use a multilingual AWE modeltrained on labelled data from well-resourced languages to spot keywords in datain the unseen target language. In contrast to ASR, the AWE approach onlyrequires a few keyword exemplars. In controlled experiments on Wolof andSwahili where training and test data are from the same domain, an ASR modeltrained on just five minutes of data outperforms the AWE approach. But in anin-the-wild test on Swahili radio broadcasts with actual hate speech keywords,the AWE model (using one minute of template data) is more robust, givingsimilar performance to an ASR system trained on 30 hours of labelled data.

Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili