Pretrained ASR Pseudo-labeling for Noisy Police Audio
Bridging the performance gap in automated transcription for high-noise emergency communication environments.
Pretrained ASR systems perform poorly on noisy Broadcast Police Communication (BPC), hindering efforts to understand police decision-making. Pseudo-labeling offers an unsupervised path to improve ASR without expensive human labels, but the efficacy of this approach on very noisy ...
ArXiv CS.AI
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Leveraging pseudo-labeling for noisy police audio bridges a critical data gap, enabling better automated transcription in environments where human labeling is costly or impractical. This advancement improves the accountability and accessibility of public safety records by refining speech recognition in high-noise conditions.
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