  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.
