  This study investigates the use of Visually Grounded Speech (VGS) models for
keyword localisation in speech. The study focusses on two main research
questions: (1) Is keyword localisation possible with VGS models and (2) Can
keyword localisation be done cross-lingually in a real low-resource setting?
Four methods for localisation are proposed and evaluated on an English dataset,
with the best-performing method achieving an accuracy of 57%. A new dataset
containing spoken captions in Yoruba language is also collected and released
for cross-lingual keyword localisation. The cross-lingual model obtains a
precision of 16% in actual keyword localisation and this performance can be
improved by initialising from a model pretrained on English data. The study
presents a detailed analysis of the model's success and failure modes and
highlights the challenges of using VGS models for keyword localisation in
low-resource settings.
