@inproceedings{choux-etal-2025-tunifra,
title = "{T}uni{F}ra: A {T}unisian {A}rabic Speech Corpus with Orthographic Transcriptions and {F}rench Translations",
author = "Choux, Alex and
Avila, Marko and
Crego, Josep and
Bougares, Fethi and
Laurent, Antoine",
editor = "Darwish, Kareem and
Ali, Ahmed and
Abu Farha, Ibrahim and
Touileb, Samia and
Zitouni, Imed and
Abdelali, Ahmed and
Al-Ghamdi, Sharefah and
Alkhereyf, Sakhar and
Zaghouani, Wajdi and
Khalifa, Salam and
AlKhamissi, Badr and
Almatham, Rawan and
Hamed, Injy and
Alyafeai, Zaid and
Alowisheq, Areeb and
Inoue, Go and
Mrini, Khalil and
Alshammari, Waad",
booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.arabicnlp-main.5/",
pages = "64--68",
ISBN = "979-8-89176-352-4",
abstract = "We introduce TuniFra, a novel and comprehensive corpus developed to advance research in Automatic Speech Recognition (ASR) and Speech-to-Text Translation (STT) for Tunisian Arabic, a notably low-resourced language variety. The TuniFra corpus comprises 15 hours of native Tunisian Arabic speech, carefully transcribed and manually translated into French. While the development of ASR and STT systems for major languages is supported by extensive datasets, low-resource languages such as Tunisian Arabic face significant challenges due to limited training data, particularly for speech technologies. TuniFra addresses this gap by offering a valuable resource tailored for both ASR and STT tasks in the Tunisian dialect. We describe our methodology for data collection, transcription, and annotation, and present initial baseline results for both Tunisian Arabic speech recognition and Tunisian Arabic{--}French speech translation."
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<abstract>We introduce TuniFra, a novel and comprehensive corpus developed to advance research in Automatic Speech Recognition (ASR) and Speech-to-Text Translation (STT) for Tunisian Arabic, a notably low-resourced language variety. The TuniFra corpus comprises 15 hours of native Tunisian Arabic speech, carefully transcribed and manually translated into French. While the development of ASR and STT systems for major languages is supported by extensive datasets, low-resource languages such as Tunisian Arabic face significant challenges due to limited training data, particularly for speech technologies. TuniFra addresses this gap by offering a valuable resource tailored for both ASR and STT tasks in the Tunisian dialect. We describe our methodology for data collection, transcription, and annotation, and present initial baseline results for both Tunisian Arabic speech recognition and Tunisian Arabic–French speech translation.</abstract>
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%0 Conference Proceedings
%T TuniFra: A Tunisian Arabic Speech Corpus with Orthographic Transcriptions and French Translations
%A Choux, Alex
%A Avila, Marko
%A Crego, Josep
%A Bougares, Fethi
%A Laurent, Antoine
%Y Darwish, Kareem
%Y Ali, Ahmed
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%Y Zitouni, Imed
%Y Abdelali, Ahmed
%Y Al-Ghamdi, Sharefah
%Y Alkhereyf, Sakhar
%Y Zaghouani, Wajdi
%Y Khalifa, Salam
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Hamed, Injy
%Y Alyafeai, Zaid
%Y Alowisheq, Areeb
%Y Inoue, Go
%Y Mrini, Khalil
%Y Alshammari, Waad
%S Proceedings of The Third Arabic Natural Language Processing Conference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-352-4
%F choux-etal-2025-tunifra
%X We introduce TuniFra, a novel and comprehensive corpus developed to advance research in Automatic Speech Recognition (ASR) and Speech-to-Text Translation (STT) for Tunisian Arabic, a notably low-resourced language variety. The TuniFra corpus comprises 15 hours of native Tunisian Arabic speech, carefully transcribed and manually translated into French. While the development of ASR and STT systems for major languages is supported by extensive datasets, low-resource languages such as Tunisian Arabic face significant challenges due to limited training data, particularly for speech technologies. TuniFra addresses this gap by offering a valuable resource tailored for both ASR and STT tasks in the Tunisian dialect. We describe our methodology for data collection, transcription, and annotation, and present initial baseline results for both Tunisian Arabic speech recognition and Tunisian Arabic–French speech translation.
%U https://aclanthology.org/2025.arabicnlp-main.5/
%P 64-68
Markdown (Informal)
[TuniFra: A Tunisian Arabic Speech Corpus with Orthographic Transcriptions and French Translations](https://aclanthology.org/2025.arabicnlp-main.5/) (Choux et al., ArabicNLP 2025)
ACL