  Acoustic word embedding models map variable duration speech segments to fixed
dimensional vectors, enabling efficient speech search and discovery. Previous
work explored how embeddings can be obtained in zero-resource settings where no
labelled data is available in the target language. The current best approach
uses transfer learning: a single supervised multilingual model is trained using
labelled data from multiple well-resourced languages and then applied to a
target zero-resource language (without fine-tuning). However, it is still
unclear how the specific choice of training languages affect downstream
performance. Concretely, here we ask whether it is beneficial to use training
languages related to the target. Using data from eleven languages spoken in
Southern Africa, we experiment with adding data from different language
families while controlling for the amount of data per language. In word
discrimination and query-by-example search evaluations, we show that training
on languages from the same family gives large improvements. Through
finer-grained analysis, we show that training on even just a single related
language gives the largest gain. We also find that adding data from unrelated
languages generally doesn't hurt performance.
