基于在线视频的犬类发声词汇分析
Towards Lexical Analysis of Dog Vocalizations via Online Videos
AI总结：
本研究通过从YouTube构建柴犬发声数据集并分析声音与上下文的条件概率，验证并细化了犬类发声的语义分类，发现现有词类型可进一步细分。
摘要：
破译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语义相关联，对犬类发声的语义进行了数据驱动的探究。我们首先提出了一个新的柴犬声音数据集，并包含位置和活动等上下文信息，该数据集通过一个构建良好的流程从YouTube收集。该框架也适用于其他动物物种。基于对犬类发声与相应位置和活动之间条件概率的分析，我们发现了支持先前关于各种犬类声音语义含义的启发式研究的证据。例如，低吼声可以表示互动。此外，我们的研究产生了新的见解，即现有的词类型可以细分为更细粒度的子类型，并且柴犬的最小语义单元与词相关。例如，呜咽声可以细分为两种类型：寻求关注和不适。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
