  Individual vocal differences are ubiquitous in the animal kingdom. In humans,
these differences pervade the entire vocal repertoire and constitute a "voice
print". Apes, our closest-living relatives, possess individual signatures
within specific call types, but the potential for a unique voice print has been
little investigated. This is partially attributed to the limitations associated
with extracting meaningful features from small data sets. Advances in machine
learning have highlighted an alternative to traditional acoustic features,
namely pre-trained learnt extractors. Here, we present an approach building on
these developments: leveraging a feature extractor based on a deep neural
network trained on over 10,000 human voice prints to provide an informative
space over which we identify chimpanzee voice prints. We compare our results
with those obtained by using traditional acoustic features and discuss the
benefits of our methodology and the significance of our findings for the
identification of "voice prints" in non-human animals.
