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

• URL: http://arxiv.org/abs/2404.18739v1
• Date: Mon, 29 Apr 2024 14:41:59 GMT
• Title: Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification
• Authors: Artem Abzaliev, Humberto Pérez Espinosa, Rada Mihalcea,
• Abstract summary: We explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks. We show that using speech embedding representations significantly improves over simpler classification baselines. We also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks.
• Score: 23.974783158267428
• License: http://creativecommons.org/licenses/by/4.0/
• 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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