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.

本文探讨了在人类语音领域预训练的自监督学习模型是否可以用于生物声学领域，结果表明可以通过这种方式成功地从黄腹掌猴的声音中识别出不同的个体身份信息。