Marais, LauretteLouw, Johannes ABadenhorst, Jacob ACCalteaux, Karen VWilken, IlanaVan Niekerk, NinaStein, Glenn2021-03-112021-03-112020-07Marais, L., Louw, J.A., Badenhorst, J.A., Calteaux, K.V., Wilken, I., Van Niekerk, N. & Stein, G. 2020. AwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health care. http://hdl.handle.net/10204/11894 .978-0-578-64709-8978-1-7281-6830-2http://hdl.handle.net/10204/11894The language contexts of multilingual developing countries such as South Africa are often characterised by communication challenges resulting from language differences. AwezaMed is a multilingual, multimodal speech-to-speech translation application for the health care domain, which was designed to assist in bridging communication barriers and mitigate the risks of miscommunication. The application focuses on the domain of maternal health care. It uses English as source language and Afrikaans, isiXhosa and isiZulu as target languages to enable health care providers to communicate with patients in their own language. It incorporates automatic speech recognition, machine translation and text-to-speech to deliver speech-to-speech translation functionality in a scalable way via a REST API to an Android mobile application. It is being piloted at various health care facilities across South Africa.AbstractenMachine translationMobile applicationSpeech-to-speech translationText-to-speechAwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health careConference PresentationMarais, L., Louw, J. A., Badenhorst, J. A., Calteaux, K. V., Wilken, I., Van Niekerk, N., & Stein, G. (2020). AwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health care. http://hdl.handle.net/10204/11894Marais, Laurette, Johannes A Louw, Jacob AC Badenhorst, Karen V Calteaux, Ilana Wilken, Nina Van Niekerk, and Glenn Stein. "AwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health care." <i>2020 IEEE 23rd International Conference on Information Fusion (FUSION), Rustenburg, South Africa, 6-9 July 2020</i> (2020): http://hdl.handle.net/10204/11894Marais L, Louw JA, Badenhorst JA, Calteaux KV, Wilken I, Van Niekerk N, et al, AwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health care; 2020. http://hdl.handle.net/10204/11894 .TY - Conference Presentation AU - Marais, Laurette AU - Louw, Johannes A AU - Badenhorst, Jacob AC AU - Calteaux, Karen V AU - Wilken, Ilana AU - Van Niekerk, Nina AU - Stein, Glenn AB - The language contexts of multilingual developing countries such as South Africa are often characterised by communication challenges resulting from language differences. AwezaMed is a multilingual, multimodal speech-to-speech translation application for the health care domain, which was designed to assist in bridging communication barriers and mitigate the risks of miscommunication. The application focuses on the domain of maternal health care. It uses English as source language and Afrikaans, isiXhosa and isiZulu as target languages to enable health care providers to communicate with patients in their own language. It incorporates automatic speech recognition, machine translation and text-to-speech to deliver speech-to-speech translation functionality in a scalable way via a REST API to an Android mobile application. It is being piloted at various health care facilities across South Africa. DA - 2020-07 DB - ResearchSpace DP - CSIR J1 - 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Rustenburg, South Africa, 6-9 July 2020 KW - Machine translation KW - Mobile application KW - Speech-to-speech translation KW - Text-to-speech LK - https://researchspace.csir.co.za PY - 2020 SM - 978-0-578-64709-8 SM - 978-1-7281-6830-2 T1 - AwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health care TI - AwezaMed: A multilingual, multimodal speech-to-speech translation application for maternal health care UR - http://hdl.handle.net/10204/11894 ER -24224