Document type scientific article published in arXiv
Authors: Artem Abzaliev, Humberto Pérez Espinosa, Rada Mihalcea
Preview: Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a wide 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.
Pre-publication resulting in an article in ELBRLO.PL on 07/06/2024
From the arXiv website
