Towards Lexical Analysis of Dog Vocalizations via Online Videos

TL;DR

This paper introduces a data-driven approach to analyze dog vocalizations using YouTube videos, revealing semantic meanings and finer distinctions in sounds, with potential applicability to other animals.

Contribution

It presents a new dataset of Shiba Inu sounds with contextual info and a framework for semantic analysis of animal vocalizations from online videos.

Findings

Growls can signify interactions.

Whimper types indicate attention-seeking or discomfort.

Existing word types can be subdivided into finer subtypes.

Abstract

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.