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

Audio De-identification: A New Entity Recognition Task