发表机构
Johns Hopkins University; Providence College（约翰斯·霍普金斯大学; 普罗维登斯学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统评估伪标注在嘈杂警务音频上的效果，提出LLM评判过滤和跨模型伪标注方法，显著降低词错误率，为未来研究指明方向。
AI 中文摘要
预训练ASR系统在嘈杂的警务广播通信（BPC）上表现不佳，阻碍了理解警务决策的努力。伪标注提供了一种无监督的路径，无需昂贵的人工标注即可改进ASR，但这种方法在非常嘈杂的领域中的有效性尚不清楚。在这项工作中，我们系统评估了伪标注的机会和限制，以将基础ASR模型（Whisper和Qwen3-ASR）适应到来自巴尔的摩和芝加哥的嘈杂BPC领域语料库。我们证明，现有的内部置信度指标（对数概率和STAR分数）无法区分高质量和低质量的BPC伪标签，并引入了一种外部LLM作为评判者的过滤范式，利用参数化知识丢弃上下文不合理的转录。我们的LLM评判比内部指标更积极地过滤，并显著降低了巴尔的摩和芝加哥BPC语料库中伪标签训练集的词错误率（WER），尽管与理想过滤器相比仍有较大差距。我们还引入了一种新的跨模型伪标注范式，其中一个模型使用来自另一个模型的伪标签进行微调，并将此方法确定为未来伪标注工作的有前景方向。
英文摘要
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 domains is not known. In this work, we systematically assess the opportunities and limits of pseudo-labeling to adapt foundation ASR models (Whisper and Qwen3-ASR) to noisy BPC domain corpora from Baltimore and Chicago. We demonstrate that existing internal confidence metrics (log-probabilities and STAR scores) fail to distinguish between high and low quality BPC pseudo-labels, and we introduce an external LLM-as-a-judge filtering paradigm that leverages parametric knowledge to discard contextually implausible transcripts. Our LLM-judging filters more aggressively than internal metrics and significantly reduces WER of the pseudo-labeled training sets across the Baltimore and Chicago BPC corpora, though a substantial gap remains relative to an oracle filter. We also introduce a new cross-model pseudo-labeling paradigm where one model is finetuned with pseudo-labels from the other, and we identify this method as a promising direction for future pseudo-labeling work.
CommentsAccpeted to SLT 2026
