End-to-End Speech Translation of Arabic to English Broadcast News

Fethi Bougares, Salim Jouili

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-to-text Machine translation (MT). In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-to-sequence 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-to-end speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.
Anthology ID:
2022.wanlp-1.29
Volume:
Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Houda Bouamor, Hend Al-Khalifa, Kareem Darwish, Owen Rambow, Fethi Bougares, Ahmed Abdelali, Nadi Tomeh, Salam Khalifa, Wajdi Zaghouani
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
312–319
Language:
URL:
https://aclanthology.org/2022.wanlp-1.29
DOI:
10.18653/v1/2022.wanlp-1.29
Bibkey:
Cite (ACL):
Fethi Bougares and Salim Jouili. 2022. End-to-End Speech Translation of Arabic to English Broadcast News. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 312–319, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
End-to-End Speech Translation of Arabic to English Broadcast News (Bougares & Jouili, WANLP 2022)
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PDF:
https://preview.aclanthology.org/naacl24-info/2022.wanlp-1.29.pdf
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