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        <title>End-to-End Speech Translation of Arabic to English Broadcast News</title>
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            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T End-to-End Speech Translation of Arabic to English Broadcast News
%A Bougares, Fethi
%A Jouili, Salim
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F bougares-jouili-2022-end
%X 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.
%R 10.18653/v1/2022.wanlp-1.29
%U https://aclanthology.org/2022.wanlp-1.29
%U https://doi.org/10.18653/v1/2022.wanlp-1.29
%P 312-319

Markdown (Informal)

[End-to-End Speech Translation of Arabic to English Broadcast News](https://aclanthology.org/2022.wanlp-1.29) (Bougares & Jouili, WANLP 2022)

• End-to-End Speech Translation of Arabic to English Broadcast News (Bougares & Jouili, WANLP 2022)

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.