A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility

Pierrette Bouillon, Johanna Gerlach, Jonathan Mutal, Nikos Tsourakis, Hervé Spechbach

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.
Anthology ID:
2021.nlp4posimpact-1.15
Volume:
Proceedings of the 1st Workshop on NLP for Positive Impact
Month:
August
Year:
2021
Address:
Online
Venue:
NLP4PosImpact
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
135–142
Language:
URL:
https://aclanthology.org/2021.nlp4posimpact-1.15
DOI:
10.18653/v1/2021.nlp4posimpact-1.15
Bibkey:
Cite (ACL):
Pierrette Bouillon, Johanna Gerlach, Jonathan Mutal, Nikos Tsourakis, and Hervé Spechbach. 2021. A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility. In Proceedings of the 1st Workshop on NLP for Positive Impact, pages 135–142, Online. Association for Computational Linguistics.
Cite (Informal):
A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility (Bouillon et al., NLP4PosImpact 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.nlp4posimpact-1.15.pdf
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