LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect

Hedi Naouara, Jean-Pierre Lorré, Jérôme Louradour
4/3/2025
cs.CLcs.SDeess.AS

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Abstract

Developing Automatic Speech Recognition (ASR) systems for Tunisian Arabic Dialect is challenging due to the dialect's linguistic complexity and the scarcity of annotated speech datasets. To address these challenges, we propose the LinTO audio and textual datasets -- comprehensive resources that capture phonological and lexical features of Tunisian Arabic Dialect. These datasets include a variety of texts from numerous sources and real-world audio samples featuring diverse speakers and code-switching between Tunisian Arabic Dialect and English or French. By providing high-quality audio paired with precise transcriptions, the LinTO audio and textual datasets aim to provide qualitative material to build and benchmark ASR systems for the Tunisian Arabic Dialect. Keywords -- Tunisian Arabic Dialect, Speech-to-Text, Low-Resource Languages, Audio Data Augmentation

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Cite this paper

@article{naouara2025linto,
  title  = {LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect},
  author = {Hedi Naouara and Jean-Pierre Lorré and Jérôme Louradour},
  year   = {2025},
  eprint = {2504.02604},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.02604v1}
}

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