  Developments in weakly supervised and self-supervised models could enable
speech technology in low-resource settings where full transcriptions are not
available. We consider whether keyword localisation is possible using two forms
of weak supervision where location information is not provided explicitly. In
the first, only the presence or absence of a word is indicated, i.e. a
bag-of-words (BoW) labelling. In the second, visual context is provided in the
form of an image paired with an unlabelled utterance; a model then needs to be
trained in a self-supervised fashion using the paired data. For keyword
localisation, we adapt a saliency-based method typically used in the vision
domain. We compare this to an existing technique that performs localisation as
a part of the network architecture. While the saliency-based method is more
flexible (it can be applied without architectural restrictions), we identify a
critical limitation when using it for keyword localisation. Of the two forms of
supervision, the visually trained model performs worse than the BoW-trained
model. We show qualitatively that the visually trained model sometimes locate
semantically related words, but this is not consistent. While our results show
that there is some signal allowing for localisation, it also calls for other
localisation methods better matched to these forms of weak supervision.
