Speech translation vs. Interpreting

Artificial intelligence (AI), deep learning technologies and big data have impacted on the interpretation market and AI-based technologies can be used in automated speech translation. The first experiments to create an automatic interpreter took place at the end of the 1980s and early 1990s. Today,...

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Autor principal:	Ildikó Horváth
Formato:	article
Lenguaje:	DE EN FR RU
Publicado:	Herzen State Pedagogical University of Russia 2021
Materias:	automatic speech translation interpreting artificial intelligence speech recognitions MT Philology. Linguistics P1-1091
Acceso en línea:	https://doaj.org/article/23d4d089779e40b99b7004400869e072
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spelling	oai:doaj.org-article:23d4d089779e40b99b7004400869e0722021-11-15T03:43:43ZSpeech translation vs. Interpreting10.33910/2686-830X-2021-3-2-174-1872686-830Xhttps://doaj.org/article/23d4d089779e40b99b7004400869e0722021-11-01T00:00:00Zhttps://www.languagestudies.ru/index.php/main/article/view/80https://doaj.org/toc/2686-830X Artificial intelligence (AI), deep learning technologies and big data have impacted on the interpretation market and AI-based technologies can be used in automated speech translation. The first experiments to create an automatic interpreter took place at the end of the 1980s and early 1990s. Today, there are several AI-based devices available on the market which attempt to fully automatize the interpreting process, both in the consecutive and in the simultaneous mode in a limited number of specific communication situations. This article first reviews the history and mechanism of automated interpreting and provides a comparison of human and automated interpreting. It also presents the main features and use cases of automated speech translation (AST). By showing that the two activities are intrinsically different, it argues that they need to be distinguished more clearly by defining the speech-to-speech (S2S) language transfer accomplished by computers as automated speech translation (AST) and keeping the term ‘interpreting’ for the human activity. Automated speech translation has an undeniable role and place in today’s world, steeped in technology and AI. However, it needs to be underlined that it is completely different from the complex interpreting service human interpreters provide and the circumstances and contexts in which its use can be advised is intrinsically different from that of human interpreting. Therefore, the real question is how AST and human interpreting can complement each other, in other words, what are the situations and contexts where AST is desired and applicable and when is there a need for human interpreting? Ildikó HorváthHerzen State Pedagogical University of Russiaarticleautomatic speech translationinterpretingartificial intelligencespeech recognitionsMTPhilology. LinguisticsP1-1091DEENFRRUИсследования языка и современное гуманитарное знание, Vol 3, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	DE EN FR RU
topic	automatic speech translation interpreting artificial intelligence speech recognitions MT Philology. Linguistics P1-1091
spellingShingle	automatic speech translation interpreting artificial intelligence speech recognitions MT Philology. Linguistics P1-1091 Ildikó Horváth Speech translation vs. Interpreting
description	Artificial intelligence (AI), deep learning technologies and big data have impacted on the interpretation market and AI-based technologies can be used in automated speech translation. The first experiments to create an automatic interpreter took place at the end of the 1980s and early 1990s. Today, there are several AI-based devices available on the market which attempt to fully automatize the interpreting process, both in the consecutive and in the simultaneous mode in a limited number of specific communication situations. This article first reviews the history and mechanism of automated interpreting and provides a comparison of human and automated interpreting. It also presents the main features and use cases of automated speech translation (AST). By showing that the two activities are intrinsically different, it argues that they need to be distinguished more clearly by defining the speech-to-speech (S2S) language transfer accomplished by computers as automated speech translation (AST) and keeping the term ‘interpreting’ for the human activity. Automated speech translation has an undeniable role and place in today’s world, steeped in technology and AI. However, it needs to be underlined that it is completely different from the complex interpreting service human interpreters provide and the circumstances and contexts in which its use can be advised is intrinsically different from that of human interpreting. Therefore, the real question is how AST and human interpreting can complement each other, in other words, what are the situations and contexts where AST is desired and applicable and when is there a need for human interpreting?
format	article
author	Ildikó Horváth
author_facet	Ildikó Horváth
author_sort	Ildikó Horváth
title	Speech translation vs. Interpreting
title_short	Speech translation vs. Interpreting
title_full	Speech translation vs. Interpreting
title_fullStr	Speech translation vs. Interpreting
title_full_unstemmed	Speech translation vs. Interpreting
title_sort	speech translation vs. interpreting
publisher	Herzen State Pedagogical University of Russia
publishDate	2021
url	https://doaj.org/article/23d4d089779e40b99b7004400869e072
work_keys_str_mv	AT ildikohorvath speechtranslationvsinterpreting
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Speech translation vs. Interpreting

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