• DocumentCode
    2501117
  • Title

    Modern standard Arabic based multilingual approach for dialectal Arabic speech recognition

  • Author

    Elmahdy, Mohamed ; Gruhn, Rainer ; Minker, Wolfgang ; Abdennadher, Slim

  • Author_Institution
    Inst. of Inf. Technol. & Germany, Ulm Univ., Ulm, Germany
  • fYear
    2009
  • fDate
    20-22 Oct. 2009
  • Firstpage
    169
  • Lastpage
    174
  • Abstract
    In this paper we are proposing a new multilingual approach for dialectal Arabic speech recognition. Dialectal Arabic is only spoken and not used in written form in almost all domains and there is no standard for dialectal Arabic transcription. Therefore, preparing large training corpora for dialectal Arabic acoustic modeling is too difficult compared to Modern Standard Arabic. We have built several acoustic models with news broadcast speech corpus of modern standard Arabic speech. Egyptian Colloquial Arabic has been chosen in our work as a typical Arabic dialect example. We have collected Egyptian Colloquial Arabic connected digits corpus to evaluate our approach. We were able to use modern standard Arabic acoustic models as multilingual models to decode Egyptian Arabic. We were able to reach a recognition rate of 99.34% which is very satisfactory compared to the monolingual approach and compared to previous work in spoken Arabic digits speech recognition.
  • Keywords
    natural languages; speech recognition; Egyptian Colloquial Arabic; dialectal Arabic speech recognition; modern standard Arabic; multilingual approach; Decoding; Information technology; Loudspeakers; Motion pictures; Natural language processing; Natural languages; Radio broadcasting; Speech recognition; TV broadcasting; Writing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Language Processing, 2009. SNLP '09. Eighth International Symposium on
  • Conference_Location
    Bangkok
  • Print_ISBN
    978-1-4244-4138-9
  • Electronic_ISBN
    978-1-4244-4139-6
  • Type

    conf

  • DOI
    10.1109/SNLP.2009.5340923
  • Filename
    5340923