  Similar to humans, animals make extensive use of verbal and non-verbal forms
of communication, including a large range of audio signals. In this paper, we
address dog vocalizations and explore the use of self-supervised speech
representation models pre-trained on human speech to address dog bark
classification tasks that find parallels in human-centered tasks in speech
recognition. We specifically address four tasks: dog recognition, breed
identification, gender classification, and context grounding. We show that
using speech embedding representations significantly improves over simpler
classification baselines. Further, we also find that models pre-trained on
large human speech acoustics can provide additional performance boosts on
several tasks.
