Police body-worn cameras have the potential to improve accountability and
transparency in policing. Yet in practice, they result in millions of hours of
footage that is never reviewed. We investigate the potential of large
pre-trained speech models for facilitating reviews, focusing on ASR and officer
speech detection in footage from traffic stops. Our proposed pipeline includes
training data alignment and filtering, fine-tuning with resource constraints,
and combining officer speech detection with ASR for a fully automated approach.
We find that (1) fine-tuning strongly improves ASR performance on officer
speech (WER=12-13%), (2) ASR on officer speech is much more accurate than on
community member speech (WER=43.55-49.07%), (3) domain-specific tasks like
officer speech detection and diarization remain challenging. Our work offers
practical applications for reviewing body camera footage and general guidance
for adapting pre-trained speech models to noisy multi-speaker domains.

本文研究如何使用大型预训练语音模型来提高警察配戴录音摄像头记录的交通站点片段的自动语音识别性能，并发现 fine-tuning 技术可以大大提高识别性能。