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Visually grounded speech (VGS) models are trained on images paired withunlabelled spoken captions. Such models could be used to build speech systemsin settings where it is impossible to get labelled data, e.g. for documentingunwritten languages. However, most VGS studies are in English or otherhigh-resource languages. This paper attempts to address this shortcoming. Wecollect and release a new single-speaker dataset of audio captions for 6kFlickr images in Yor\`ub\'a -- a real low-resource language spoken in Nigeria.We train an attention-based VGS model where images are automatically taggedwith English visual labels and paired with Yor\`ub\'a utterances. This enablescross-lingual keyword localisation: a written English query is detected andlocated in Yor\`ub\'a speech. To quantify the effect of the smaller dataset, wecompare to English systems trained on similar and more data. We hope that thisnew dataset will stimulate research in the use of VGS models for reallow-resource languages.

YFACC: A Yor\`ub\'a speech-image dataset for cross-lingual keyword localisation through visual grounding