@inproceedings{bouillon-etal-2021-speech,
title = "A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility",
author = "Bouillon, Pierrette and
Gerlach, Johanna and
Mutal, Jonathan and
Tsourakis, Nikos and
Spechbach, Herv{\'e}",
editor = "Field, Anjalie and
Prabhumoye, Shrimai and
Sap, Maarten and
Jin, Zhijing and
Zhao, Jieyu and
Brockett, Chris",
booktitle = "Proceedings of the 1st Workshop on NLP for Positive Impact",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4posimpact-1.15",
doi = "10.18653/v1/2021.nlp4posimpact-1.15",
pages = "135--142",
abstract = "In this overview article we describe an application designed to enable communication between health practitioners and patients who do not share a common language, in situations where professional interpreters are not available. Built on the principle of a fixed phrase translator, the application implements different natural language processing (NLP) technologies, such as speech recognition, neural machine translation and text-to-speech to improve usability. Its design allows easy portability to new domains and integration of different types of output for multiple target audiences. Even though BabelDr is far from solving the problem of miscommunication between patients and doctors, it is a clear example of NLP in a real world application designed to help minority groups to communicate in a medical context. It also gives some insights into the relevant criteria for the development of such an application.",
}
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%0 Conference Proceedings
%T A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility
%A Bouillon, Pierrette
%A Gerlach, Johanna
%A Mutal, Jonathan
%A Tsourakis, Nikos
%A Spechbach, Hervé
%Y Field, Anjalie
%Y Prabhumoye, Shrimai
%Y Sap, Maarten
%Y Jin, Zhijing
%Y Zhao, Jieyu
%Y Brockett, Chris
%S Proceedings of the 1st Workshop on NLP for Positive Impact
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F bouillon-etal-2021-speech
%X In this overview article we describe an application designed to enable communication between health practitioners and patients who do not share a common language, in situations where professional interpreters are not available. Built on the principle of a fixed phrase translator, the application implements different natural language processing (NLP) technologies, such as speech recognition, neural machine translation and text-to-speech to improve usability. Its design allows easy portability to new domains and integration of different types of output for multiple target audiences. Even though BabelDr is far from solving the problem of miscommunication between patients and doctors, it is a clear example of NLP in a real world application designed to help minority groups to communicate in a medical context. It also gives some insights into the relevant criteria for the development of such an application.
%R 10.18653/v1/2021.nlp4posimpact-1.15
%U https://aclanthology.org/2021.nlp4posimpact-1.15
%U https://doi.org/10.18653/v1/2021.nlp4posimpact-1.15
%P 135-142
Markdown (Informal)
[A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility](https://aclanthology.org/2021.nlp4posimpact-1.15) (Bouillon et al., NLP4PI 2021)
ACL