Datastore Design for Analysis of Police Broadcast Audio at Scale

Abstract

With policing coming under greater scrutiny in recent years, researchers have begun to more thoroughly study the effects of contact between police and minority communities. Despite data archives of hundreds of thousands of recorded Broadcast Police Communications (BPC) being openly available to the public, a closer look at a large-scale analysis of the language of policing has remained largely unexplored. While this research is critical in understanding a "pre-reflective" notion of policing, the large quantity of data presents numerous challenges in its organization and analysis. In this paper, we describe preliminary work towards enabling Speech Emotion Recognition (SER) in an analysis of the Chicago Police Department's (CPD) BPC by demonstrating the pipelined creation of a datastore to enable a multimodal analysis of composed raw audio files