@inproceedings{mani-etal-2020-towards,
title = "Towards Understanding {ASR} Error Correction for Medical Conversations",
author = "Mani, Anirudh and
Palaskar, Shruti and
Konam, Sandeep",
editor = "Bhatia, Parminder and
Lin, Steven and
Gangadharaiah, Rashmi and
Wallace, Byron and
Shafran, Izhak and
Shivade, Chaitanya and
Du, Nan and
Diab, Mona",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpmc-1.2/",
doi = "10.18653/v1/2020.nlpmc-1.2",
pages = "7--11",
abstract = "Domain Adaptation for Automatic Speech Recognition (ASR) error correction via machine translation is a useful technique for improving out-of-domain outputs of pre-trained ASR systems to obtain optimal results for specific in-domain tasks. We use this technique on our dataset of Doctor-Patient conversations using two off-the-shelf ASR systems: Google ASR (commercial) and the ASPIRE model (open-source). We train a Sequence-to-Sequence Machine Translation model and evaluate it on seven specific UMLS Semantic types, including Pharmacological Substance, Sign or Symptom, and Diagnostic Procedure to name a few. Lastly, we breakdown, analyze and discuss the 7{\%} overall improvement in word error rate in view of each Semantic type."
}
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<abstract>Domain Adaptation for Automatic Speech Recognition (ASR) error correction via machine translation is a useful technique for improving out-of-domain outputs of pre-trained ASR systems to obtain optimal results for specific in-domain tasks. We use this technique on our dataset of Doctor-Patient conversations using two off-the-shelf ASR systems: Google ASR (commercial) and the ASPIRE model (open-source). We train a Sequence-to-Sequence Machine Translation model and evaluate it on seven specific UMLS Semantic types, including Pharmacological Substance, Sign or Symptom, and Diagnostic Procedure to name a few. Lastly, we breakdown, analyze and discuss the 7% overall improvement in word error rate in view of each Semantic type.</abstract>
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%0 Conference Proceedings
%T Towards Understanding ASR Error Correction for Medical Conversations
%A Mani, Anirudh
%A Palaskar, Shruti
%A Konam, Sandeep
%Y Bhatia, Parminder
%Y Lin, Steven
%Y Gangadharaiah, Rashmi
%Y Wallace, Byron
%Y Shafran, Izhak
%Y Shivade, Chaitanya
%Y Du, Nan
%Y Diab, Mona
%S Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F mani-etal-2020-towards
%X Domain Adaptation for Automatic Speech Recognition (ASR) error correction via machine translation is a useful technique for improving out-of-domain outputs of pre-trained ASR systems to obtain optimal results for specific in-domain tasks. We use this technique on our dataset of Doctor-Patient conversations using two off-the-shelf ASR systems: Google ASR (commercial) and the ASPIRE model (open-source). We train a Sequence-to-Sequence Machine Translation model and evaluate it on seven specific UMLS Semantic types, including Pharmacological Substance, Sign or Symptom, and Diagnostic Procedure to name a few. Lastly, we breakdown, analyze and discuss the 7% overall improvement in word error rate in view of each Semantic type.
%R 10.18653/v1/2020.nlpmc-1.2
%U https://aclanthology.org/2020.nlpmc-1.2/
%U https://doi.org/10.18653/v1/2020.nlpmc-1.2
%P 7-11
Markdown (Informal)
[Towards Understanding ASR Error Correction for Medical Conversations](https://aclanthology.org/2020.nlpmc-1.2/) (Mani et al., NLPMC 2020)
ACL