• Title of article

    Modern standard Arabic speech corpus for implementing and evaluating automatic continuous speech recognition systems

  • Author/Authors

    Abushariah، نويسنده , , Mohammad Abd-Alrahman Mahmoud and Ainon، نويسنده , , Raja Noor and Zainuddin، نويسنده , , Roziati and Alqudah، نويسنده , , Assal Ali Mustafa and Elshafei Ahmed، نويسنده , , Moustafa and Khalifa، نويسنده , , Mohammad Othman Omran، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2012
  • Pages
    28
  • From page
    2215
  • To page
    2242
  • Abstract
    This paper presents our work towards developing a new speech corpus for Modern Standard Arabic (MSA), which can be used for implementing and evaluating Arabic speaker-independent, large vocabulary, automatic, and continuous speech recognition systems. The speech corpus was recorded by 40 (20 male and 20 female) Arabic native speakers from 11 countries representing three major regions (Levant, Gulf, and Africa). Three development phases were conducted based on the size of training data, Gaussian mixture distributions, and tied states (senones). Based on our third development phase using 11 hours of training speech data, the acoustic model is composed of 16 Gaussian mixture distributions and the state distributions tied to 300 senones. Using three different data sets, the third development phase obtained 94.32% and 8.10% average word recognition correctness rate and average Word Error Rate (WER), respectively, for same speakers with different sentences (testing sentences). For different speakers with same sentences (training sentences), this work obtained 98.10% and 2.67% average word recognition correctness rate and average WER, respectively, whereas for different speakers with different sentences (testing sentences) this work obtained 93.73% and 8.75% average word recognition correctness rate and average WER, respectively.
  • Journal title
    Journal of the Franklin Institute
  • Serial Year
    2012
  • Journal title
    Journal of the Franklin Institute
  • Record number

    1544293