  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.
