语音识别用于分析警察无线电通信
Tejes Srivastava1, Ju-Chieh Chou2, Priyank Shroff1
1University of Chicago, Chicago, IL.
SLT ... : ... IEEE Workshop on Spoken Language Technology : proceedings. IEEE Workshop on Spoken Language Technology
|June 23, 2025
概括
警方无线电传输的自动语音识别 (ASR) 是一个挑战. 精心调整的模型显示了转录广播警察通讯 (BPC) 的前景,接近人类的准确性.
科学领域:
- 语音识别 语音识别 语言识别
- 计算语言学 计算语言学
- 公共安全通讯公共安全通讯
背景情况:
- 通过双向无线电广播警察通信 (BPC) 对警察协调和应急响应至关重要.
- 这些音频BPC在很大程度上没有被转录,由于具有挑战性的自然音频特性,限制了它们的分析潜力.
- BPC的自动转录很困难,阻碍了提取有价值的信息.
研究的目的:
- 评估用于广播警察通信 (BPC) 的自动语音识别 (ASR) 的可行性.
- 评估各种ASR模型的性能,包括现成的,微调的和定制的端到端模型.
- 在警察无线电领域建立ASR绩效的基准,并确定未来研究的领域.
主要方法:
- 收集了大约62,000个手动转录的警察无线电传输 (大约46小时的音频).
- 评估了大型,现成的ASR模型的性能.
- 训练和评估ASR模型,专门针对BPC集体进行微调,并开发定制的端到端模型.
主要成果:
- 人类和机器对BPC的转录都是具有挑战性的.
- 大型,现成的ASR模型在BPC数据上表现不佳.
- 在BPC数据上微调的ASR模型实现了与人类转录水平相当的性能.
结论:
- 对特定领域数据进行微调的ASR模型显著提高了广播警察通信的转录精度.
- 需要进一步的研究来解决诸如短语和警察无线电中潜在的错误通信等挑战.
- 发布的语料库和注释管道将促进未来的警察通信分析研究.
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