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.- Anthology ID:
- 2024.lrec-main.1432
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 16480–16486
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1432
- DOI:
- Cite (ACL):
- Artem Abzaliev, Humberto Perez-Espinosa, and Rada Mihalcea. 2024. Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16480–16486, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification (Abzaliev et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1432.pdf