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.

翻译：近年来，随着警务工作受到越来越多的审查，研究人员开始更深入地研究警察与少数族裔社区接触的影响。尽管公开可用的警务广播通信（BPC）录音档案已积累数十万条记录，但在大规模分析警务语言方面仍鲜有深入探索。虽然此类研究对于理解警务的"前反思"概念至关重要，但海量数据在组织与分析层面提出了诸多挑战。本文通过展示构建流水线式数据存储以实现合成原始音频文件多模态分析的初步工作，旨在推动芝加哥警察局（CPD）警务广播通信中语音情感识别（SER）研究的发展。