Datastore Design for Analysis of Police Broadcast Audio at Scale

Student: Ayah Ahmad (University of California, Berkeley)
Supervisor: Christopher Graziul (University of Chicago)

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.

We describe preliminary work toward 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.

ACM-SRC Semi-Finalist: no

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