  Self-supervised learning (SSL) models use only the intrinsic structure of a
given signal, independent of its acoustic domain, to extract essential
information from the input to an embedding space. This implies that the utility
of such representations is not limited to modeling human speech alone. Building
on this understanding, this paper explores the cross-transferability of SSL
neural representations learned from human speech to analyze bio-acoustic
signals. We conduct a caller discrimination analysis and a caller detection
study on Marmoset vocalizations using eleven SSL models pre-trained with
various pretext tasks. The results show that the embedding spaces carry
meaningful caller information and can successfully distinguish the individual
identities of Marmoset callers without fine-tuning. This demonstrates that
representations pre-trained on human speech can be effectively applied to the
bio-acoustics domain, providing valuable insights for future investigations in
this field.
