Phonetic and Lexical Discovery of a Canine Language using HuBERT
This work addresses the challenge of understanding canine communication for researchers and pet owners, but it appears incremental as it applies an existing self-supervised method to a new domain.
The paper tackles the problem of analyzing dog vocalizations for potential communication patterns by using HuBERT to classify phonemes and identify vocal patterns suggesting a rudimentary vocabulary, achieving significant acoustic consistency across all observed sequences. It also develops a web-based system for users to upload dog audio and highlight phoneme n-grams from the vocabulary.
This paper delves into the pioneering exploration of potential communication patterns within dog vocalizations and transcends traditional linguistic analysis barriers, which heavily relies on human priori knowledge on limited datasets to find sound units in dog vocalization. We present a self-supervised approach with HuBERT, enabling the accurate classification of phoneme labels and the identification of vocal patterns that suggest a rudimentary vocabulary within dog vocalizations. Our findings indicate a significant acoustic consistency in these identified canine vocabulary, covering the entirety of observed dog vocalization sequences. We further develop a web-based dog vocalization labeling system. This system can highlight phoneme n-grams, present in the vocabulary, in the dog audio uploaded by users.