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.

本文研究狗的声音，并探索利用在人类语音上预训练的自监督语音表示模型来解决狗吠分类任务，该任务与人类语音识别中的任务有相似之处。我们具体研究了狗的识别、品种鉴定、性别分类和语境关联四个任务，并表明使用语音嵌入表示可以显著改善简单分类基线。此外，我们还发现在几个任务中，预训练于大规模人类语音声学的模型可以提供额外的性能提升。