@inproceedings{bryant-2022-machine,
title = "Machine Assistance in the Real World",
author = "Bryant, Dave",
editor = "Campbell, Janice and
Larocca, Stephen and
Marciano, Jay and
Savenkov, Konstantin and
Yanishevsky, Alex",
booktitle = "Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-upg.6",
pages = "70--83",
abstract = "We have all seen the successes of Machine Assisted captioning, translation, and voiceovers and we have also seen the embarrassing errors of the same engines. Real-life usage, of course, is somewhere between the two. This session will show a couple of real-life examples of Speech To Text (STT), Machine Translation (MT) and Text To Speech (TTS) using Neural voices. We will look at what you would expect to be a perfect candidate for Automatic Speech Recognition (ASR) using multiple commercial engines and then seeing how well they can be transferred to a multiple MT engines. We will also see how its usage in AudioVisual Translation is different from a standard text translation. I will also give a brief demo of how well modern neural voices perform in multiple languages based on input from AVT timed text (vtt) format files.",
}
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<abstract>We have all seen the successes of Machine Assisted captioning, translation, and voiceovers and we have also seen the embarrassing errors of the same engines. Real-life usage, of course, is somewhere between the two. This session will show a couple of real-life examples of Speech To Text (STT), Machine Translation (MT) and Text To Speech (TTS) using Neural voices. We will look at what you would expect to be a perfect candidate for Automatic Speech Recognition (ASR) using multiple commercial engines and then seeing how well they can be transferred to a multiple MT engines. We will also see how its usage in AudioVisual Translation is different from a standard text translation. I will also give a brief demo of how well modern neural voices perform in multiple languages based on input from AVT timed text (vtt) format files.</abstract>
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%0 Conference Proceedings
%T Machine Assistance in the Real World
%A Bryant, Dave
%Y Campbell, Janice
%Y Larocca, Stephen
%Y Marciano, Jay
%Y Savenkov, Konstantin
%Y Yanishevsky, Alex
%S Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
%D 2022
%8 September
%I Association for Machine Translation in the Americas
%C Orlando, USA
%F bryant-2022-machine
%X We have all seen the successes of Machine Assisted captioning, translation, and voiceovers and we have also seen the embarrassing errors of the same engines. Real-life usage, of course, is somewhere between the two. This session will show a couple of real-life examples of Speech To Text (STT), Machine Translation (MT) and Text To Speech (TTS) using Neural voices. We will look at what you would expect to be a perfect candidate for Automatic Speech Recognition (ASR) using multiple commercial engines and then seeing how well they can be transferred to a multiple MT engines. We will also see how its usage in AudioVisual Translation is different from a standard text translation. I will also give a brief demo of how well modern neural voices perform in multiple languages based on input from AVT timed text (vtt) format files.
%U https://aclanthology.org/2022.amta-upg.6
%P 70-83
Markdown (Informal)
[Machine Assistance in the Real World](https://aclanthology.org/2022.amta-upg.6) (Bryant, AMTA 2022)
ACL
- Dave Bryant. 2022. Machine Assistance in the Real World. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 70–83, Orlando, USA. Association for Machine Translation in the Americas.