ACL 2022short29 citations
PriMock57: A Dataset Of Primary Care Mock Consultations
Alex Papadopoulos Korfiatis, Francesco Moramarco, Radmila Sarac, Aleksandar Savkov
Abstract
Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably produce automatic transcripts of clinician-patient conversations. However, access to clinical datasets is heavily restricted due to patient privacy, thus slowing down normal research practices. We detail the development of a public access, high quality dataset comprising of 57 mocked primary care consultations, including audio recordings, their manual utterance-level transcriptions, and the associated consultation notes. Our work illustrates how the dataset can be used as a benchmark for conversational medical ASR as well as consultation note generation from transcripts.
BibTeX
@inproceedings{papadopoulos-korfiatis-etal-2022-primock57,
title = "{P}ri{M}ock57: A Dataset Of Primary Care Mock Consultations",
author = "Papadopoulos Korfiatis, Alex and
Moramarco, Francesco and
Sarac, Radmila and
Savkov, Aleksandar",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.65/",
doi = "10.18653/v1/2022.acl-short.65",
pages = "588--598"
}