Hey, Soldier: Analyzing Body Camera Audio to Surface De-Escalation Tactics
Mark is now an agent in Homeland Security, but he started his law enforcement days as a beat cop in a major city. He mostly focused on underprivileged neighborhoods in the city and would go around the blocks, do his patrol, and try his best to carry a friendly demeanor. But, after a few weeks on the job, Mark couldn’t seem to get any respect or positive response from anyone in the neighborhood.
He went to one of the more senior cops he worked with and got a little piece of advice — if you want to be effective with young guys on the street who are involved with gangs or dealing drugs, you have to acknowledge the world they come from. His colleague gave him this little tip — next time you say hi, try calling them soldier. It acknowledges their experience in the world.
So he did. And just that simple change in language helped him build relationships with that community.
It should come as no surprise to anyone listening that policing in America and across the world is under the magnifying glass. Tactics like this are just one example of a small change police interaction that could help save lives across the board.
At Truleo, we asked ourselves how can we play a role in helping to improve policing? How can our technology help surface these ideas?
Building Models to Surface Negative Interactions
If you’ve ever listened to a body cam video, you’ll know that the audio track is a mess. It’s noisy, full of multiple speakers, and (no surprise) challenging for machines to transcribe. Body cam transcription can provide valuable evidence for criminal investigations, but accuracy often holds back its value.
However, in order for a machine to understand what’s happening within a body cam audio track, it doesn’t need 100% accurate transcription. Similar to a human being, Truleo's audio analytics and natural language processing platform can understand if a person is expressing negative sentiment, is nervous or not confident, or is trying to de-escalate a situation, all without having 100% accurate transcription of the words being spoken.