Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification
<|reference_start|>Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification: 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.<|reference_end|>
@article{abzaliev2024towards,
    title={Towards Dog Bark Decoding: Leveraging Human Speech Processing for
  Automated Bark Classification},
    author={Artem Abzaliev, Humberto P'erez Espinosa, Rada Mihalcea},
    journal={arXiv preprint arXiv:2404.18739},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18739},
    primaryClass={cs.CL}
}