Language Resources and Evaluation

, Volume 52, Issue 1, pp 249–267 | Cite as

Automatic speech recognition system for Tunisian dialect

  • Abir Masmoudi
  • Fethi Bougares
  • Mariem Ellouze
  • Yannick Estève
  • Lamia Belguith
Original Paper


Although Modern Standard Arabic is taught in schools and used in written communication and TV/radio broadcasts, all informal communication is typically carried out in dialectal Arabic. In this work, we focus on the design of speech tools and resources required for the development of an Automatic Speech Recognition system for the Tunisian dialect. The development of such a system faces the challenges of the lack of annotated resources and tools, apart from the lack of standardization at all linguistic levels (phonological, morphological, syntactic and lexical) together with the mispronunciation dictionary needed for ASR development. In this paper, we present a historical overview of the Tunisian dialect and its linguistic characteristics. We also describe and evaluate our rule-based phonetic tool. Next, we go deeper into the details of Tunisian dialect corpus creation. This corpus is finally approved and used to build the first ASR system for Tunisian dialect with a Word Error Rate of 22.6%.


Under-resourced language Rule-based Grapheme-to-phoneme conversion Automatic speech recognition Tunisian dialect 


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Abir Masmoudi
    • 1
    • 2
  • Fethi Bougares
    • 1
  • Mariem Ellouze
    • 2
  • Yannick Estève
    • 1
  • Lamia Belguith
    • 2
  1. 1.LIUM, Le Mans UniversityLe MansFrance
  2. 2.ANLP Research group, MIRACL Lab.University of SfaxSfaxTunisia

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