• DocumentCode
    2132820
  • Title

    Implementation and evaluation of statistical parametric speech synthesis methods for the Persian language

  • Author

    Bahaadini, Sara ; Sameti, Hossein ; Khorram, Soheil

  • Author_Institution
    Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran, Iran
  • fYear
    2011
  • fDate
    18-21 Sept. 2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Scattered and little research in the field of Persian speech synthesis systems has been performed during the last ten years. Comprehensive framework that properly implements and adapts statistical speech synthesis methods for Persian has not been conducted yet. In this paper, recent statistical parametric speech synthesis methods including CLUSTERGEN, traditional HMM-based speech synthesis and its STRAIGHT version, are implemented and adapted for Persian language. CCR test is carried out to compare these methods with each other and with unit selection method. Listeners Score samples based on CMOS. The methods were ranked by averaging the CCR scores. The results show that STRAIGHT-based system produces the best quality. Traditional HMM-based and unit selection are second and third in quality ranking. These approximately produce the same quality. Finally CLUSTERGEN produces the worst quality among these four systems.
  • Keywords
    hidden Markov models; natural language processing; speech synthesis; CLUSTERGEN; HMM-based speech synthesis; Persian language; Persian speech synthesis system; quality ranking; statistical parametric speech synthesis; statistical speech synthesis; unit selection; Adaptation models; CMOS integrated circuits; Databases; Hidden Markov models; Speech; Speech synthesis; Training; CCR test; Persian language; speech synthesis; statistical parametric; text to speech;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
  • Conference_Location
    Santander
  • ISSN
    1551-2541
  • Print_ISBN
    978-1-4577-1621-8
  • Electronic_ISBN
    1551-2541
  • Type

    conf

  • DOI
    10.1109/MLSP.2011.6064608
  • Filename
    6064608