Srivastava Tejes, Chou Ju-Chieh, Shroff Priyank, Livescu Karen, Graziul Christopher
University of Chicago, Chicago, IL.
Toyota Technological Institute at Chicago, Chicago, IL.
SLT Workshop Spok Lang Technol. 2024 Dec;2024:906-912. doi: 10.1109/slt61566.2024.10832157.
Police departments around the world use two-way radio for coordination. These broadcast police communications (BPC) are a unique source of information about everyday police activity and emergency response. Yet BPC are not transcribed, and their naturalistic audio properties make automatic transcription challenging. We collect a corpus of roughly 62,000 manually transcribed radio transmissions (46 hours of audio) to evaluate the feasibility of automatic speech recognition (ASR) using modern recognition models. We evaluate the performance of off-the-shelf speech recognizers, models fine-tuned on BPC data, and customized end-to-end models. We find that both human and machine transcription is challenging in this domain. Large off-the-shelf ASR models perform poorly, but fine-tuned models can reach the approximate range of human performance. Our work suggests directions for future work, including analysis of short utterances and potential miscommunication in police radio interactions. We make our corpus and data annotation pipeline available to other researchers, to enable further research on recognition and analysis of police communication.
世界各地的警察部门使用双向无线电进行协调。这些广播警察通信（BPC）是有关日常警察活动和应急响应的独特信息来源。然而，BPC没有被转录，并且其自然主义的音频特性使得自动转录具有挑战性。我们收集了一个包含大约62000条手动转录的无线电传输记录（46小时音频）的语料库，以评估使用现代识别模型进行自动语音识别（ASR）的可行性。我们评估了现成语音识别器、在BPC数据上微调的模型以及定制的端到端模型的性能。我们发现在这个领域，人工转录和机器转录都具有挑战性。大型现成的ASR模型表现不佳，但经过微调的模型可以达到接近人类的性能范围。我们的工作为未来的工作指明了方向，包括对警察无线电交互中的简短话语和潜在误解进行分析。我们将我们的语料库和数据标注管道提供给其他研究人员，以促进对警察通信识别和分析的进一步研究。
