Year: 2022
Type: conference-paper
Abstract: Speech translation (ST) is the task of directly translating acoustic speech signals in a source language into text in a foreign language.ST task has been addressed, for a long time, using a pipeline approach with two modules : first an Automatic Speech Recognition (ASR) in the source language followed by a text-totext Machine translation (MT).In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-tosequence deep neural network models.This paper presents our efforts towards the development of the first Broadcast News end-to-end Arabic to English speech translation system.Starting from independent ASR and MT LDC releases, we were able to identify about 92 hours of Arabic audio recordings for which the manual transcription was also translated into English at the segment level.These data was used to train and compare pipeline and end-toend speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.
Authors: Fethi Bougares, Salim Jouili
Institution: Le Mans Université
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: European Commission
OpenAlex ID: W4385573107