Pérez González de Martos, Alejandro ; Silvestre-Cerdà, Joan Albert ; Valor Miró, Juan Daniel ; Civera, Jorge ; Juan, Alfons MLLP Transcription and Translation Platform Miscellaneous 2015, (Short paper for demo presentation accepted at 10th European Conf. on Technology Enhanced Learning (EC-TEL 2015), Toledo (Spain), 2015.). Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Docencia en Red, Document translation, Efficient video subtitling, Machine Translation, MLLP, Post-editing, Video Lectures @misc{mllpttp,
title = {MLLP Transcription and Translation Platform},
author = {Pérez González de Martos, Alejandro and Silvestre-Cerdà, Joan Albert and Valor Miró, Juan Daniel and Civera, Jorge and Juan, Alfons},
url = {http://hdl.handle.net/10251/65747
http://www.mllp.upv.es/wp-content/uploads/2015/09/ttp_platform_demo_ectel2015.pdf
http://ectel2015.httc.de/index.php?id=722},
year = {2015},
date = {2015-09-16},
booktitle = {Tenth European Conference On Technology Enhanced Learning (EC-TEL 2015)},
abstract = {This paper briefly presents the main features of MLLP’s Transcription and Translation Platform, which uses state-of-the-art automatic speech recognition and machine translation systems to generate multilingual subtitles of educational audiovisual and textual content. It has proven to reduce user effort up to 1/3 of the time needed to generate transcriptions and translations from scratch.},
note = {Short paper for demo presentation accepted at 10th European Conf. on Technology Enhanced Learning (EC-TEL 2015), Toledo (Spain), 2015.},
keywords = {Automatic Speech Recognition, Docencia en Red, Document translation, Efficient video subtitling, Machine Translation, MLLP, Post-editing, Video Lectures},
pubstate = {published},
tppubtype = {misc}
}
This paper briefly presents the main features of MLLP’s Transcription and Translation Platform, which uses state-of-the-art automatic speech recognition and machine translation systems to generate multilingual subtitles of educational audiovisual and textual content. It has proven to reduce user effort up to 1/3 of the time needed to generate transcriptions and translations from scratch. |