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Self-supervised learning (SSL) models use only the intrinsic structure of agiven signal, independent of its acoustic domain, to extract essentialinformation from the input to an embedding space. This implies that the utilityof such representations is not limited to modeling human speech alone. Buildingon this understanding, this paper explores the cross-transferability of SSLneural representations learned from human speech to analyze bio-acousticsignals. We conduct a caller discrimination analysis and a caller detectionstudy on Marmoset vocalizations using eleven SSL models pre-trained withvarious pretext tasks. The results show that the embedding spaces carrymeaningful caller information and can successfully distinguish the individualidentities of Marmoset callers without fine-tuning. This demonstrates thatrepresentations pre-trained on human speech can be effectively applied to thebio-acoustics domain, providing valuable insights for future investigations inthis field.

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?