Automatic Conflict Detection in Police Body-Worn Audio
Alistair Letcher, Jelena Tri\v{s}ovi\'c, Collin Cademartori, Xi Chen,, Jason Xu

TL;DR
This paper presents a new audio processing pipeline that improves conflict detection in police body-worn audio by using adaptive noise removal, non-speech filtering, and novel conflict measures based on speech repetition and intensity.
Contribution
The study introduces a tailored pipeline with innovative conflict metrics that outperform traditional methods in noisy, real-world police audio scenarios.
Findings
Effective conflict detection on police body-worn audio
Improved metrics based on phrase repetition and intensity
Demonstrated on real LAPD data
Abstract
Automatic conflict detection has grown in relevance with the advent of body-worn technology, but existing metrics such as turn-taking and overlap are poor indicators of conflict in police-public interactions. Moreover, standard techniques to compute them fall short when applied to such diversified and noisy contexts. We develop a pipeline catered to this task combining adaptive noise removal, non-speech filtering and new measures of conflict based on the repetition and intensity of phrases in speech. We demonstrate the effectiveness of our approach on body-worn audio data collected by the Los Angeles Police Department.
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Taxonomy
TopicsSpeech and Audio Processing · Speech Recognition and Synthesis · Music and Audio Processing
