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.

翻译：解读动物语言的语义一直是一项重大挑战。本研究通过将不同声音类型与一致的语义相关联，提出了一种数据驱动的犬类发声语义研究方法。我们首先利用精心设计的流程从YouTube收集了一个新的柴犬声音数据集，并附带了地点和活动等上下文信息。该框架也适用于其他动物物种。基于犬类发声与对应地点和活动之间的条件概率分析，我们发现了支持先前关于各种犬类声音语义的启发式研究的证据。例如，低吼声可能表示互动。此外，我们的研究还产生了新见解：现有词类可以细分为更精细的子类型，且柴犬的最小语义单元与词汇相关。例如，呜咽声可细分为两种类型：寻求关注和不适。