  Deciphering the semantics of animal language has been a grand challenge. This
study presents a data-driven investigation into the semantics of dog
vocalizations via correlating different sound types with consistent semantics.
We first present a new dataset of Shiba Inu sounds, along with contextual
information such as location and activity, collected from YouTube with a
well-constructed pipeline. The framework is also applicable to other animal
species. Based on the analysis of conditioned probability between dog
vocalizations and corresponding location and activity, we discover supporting
evidence for previous heuristic research on the semantic meaning of various dog
sounds. For instance, growls can signify interactions. Furthermore, our study
yields new insights that existing word types can be subdivided into
finer-grained subtypes and minimal semantic unit for Shiba Inu is word-related.
For example, whimper can be subdivided into two types, attention-seeking and
discomfort.
