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arXiv · 2404.18739

Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification

Artem Abzaliev·Humberto Pérez Espinosa·Rada Mihalcea
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Abstract

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.

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Artem Abzaliev, Humberto Pérez Espinosa, Rada Mihalcea. 2024-04-29. Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification. https://arxiv.org/abs/2404.18739

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