Named Entity Recognition (NER) has been mostly studied in the context of
written text. Specifically, NER is an important step in de-identification
(de-ID) of medical records, many of which are recorded conversations between a
patient and a doctor. In such recordings, audio spans with personal information
should be redacted, similar to the redaction of sensitive character spans in
de-ID for written text. The application of NER in the context of audio
de-identification has yet to be fully investigated. To this end, we define the
task of audio de-ID, in which audio spans with entity mentions should be
detected. We then present our pipeline for this task, which involves Automatic
Speech Recognition (ASR), NER on the transcript text, and text-to-audio
alignment. Finally, we introduce a novel metric for audio de-ID and a new
evaluation benchmark consisting of a large labeled segment of the Switchboard
and Fisher audio datasets and detail our pipeline's results on it.

该文定义了语音去识别中的实体提取任务，并提出了一种包括音频转录、实体信息提取和音频流对齐等环节的处理流程，并基于 Switchboard 和 Fisher 音频数据集提出了一个新的音频去识别度量标准和评估基准，并对其结果进行了详细说明。