预训练ASR伪标签在嘈杂警察音频中的应用

arXiv cs.AI 论文
asr pseudo-labeling noisy-audio police-audio pretrained-models llm-as-judge

摘要

本文系统评估了伪标签技术在适配预训练ASR模型（如Whisper和Qwen3-ASR）到嘈杂警察音频中的应用，引入了一种LLM-as-a-judge过滤方法和跨模型范式，以降低词错误率。

arXiv:2609.30469v1 Announce Type: new Abstract: 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.
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# Pretrained ASR Pseudo-labeling for Noisy Police Audio
Source: [https://arxiv.org/abs/2609.30469](https://arxiv.org/abs/2609.30469)
[View PDF](https://arxiv.org/pdf/2609.30469)

> Abstract: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\.

## Submission history

From: Kaavya Chaparala \[[view email](https://arxiv.org/show-email/fd056099/2609.30469)\] **\[v1\]**Thu, 24 Sep 2026 19:05:53 UTC \(8,461 KB\)

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