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arXiv · 1903.07037

Audio De-identification: A New Entity Recognition Task

Ido Cohn·Itay Laish·Genady Beryozkin·Gang Li·Izhak Shafran·Idan Szpektor·Tzvika Hartman·Avinatan Hassidim·Yossi Matias
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Abstract

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.

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Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias. 2019-03-17. Audio De-identification: A New Entity Recognition Task. https://arxiv.org/abs/1903.07037

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