• DocumentCode
    3662917
  • Title

    Statistical Parametric Speech Synthesis: A review

  • Author

    Athira Aroon;S.B Dhonde

  • Author_Institution
    Department of Electronics Engineering, A.I.S.S.M.S Institute of Information Technology, Pune, India
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In this paper we have briefly reviewed the Statistical Parametric Speech Synthesis (SPSS ), based on hidden Markov model. The non-mathematical introduction of SPSS have been introduced. Have emphasized the recent emerging techniques used in SPSS like Autoregressive HMM model, Gaussian Process Regression(GPR), Neural Autoregressive Distribution Estimators (NADE) overcoming Restricted Boltzmann Machines (RBM), Deep Neural Networks (DNNs). One of the major drawback of SPSS is vocoder quality in accordance to this problem we have analyzed spectral envelope estimation algorithms proposed for speech synthesis like STRAIGHT, TANDEM-STRAIGHT, CHEAPTRICK providing high quality.).
  • Keywords
    "Hidden Markov models","Speech","Trajectory","Frequency-domain analysis","Markov processes","Indexes","Analytical models"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Control (ISCO), 2015 IEEE 9th International Conference on
  • Type

    conf

  • DOI
    10.1109/ISCO.2015.7282379
  • Filename
    7282379