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
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"
Conference_Titel :
Intelligent Systems and Control (ISCO), 2015 IEEE 9th International Conference on
DOI :
10.1109/ISCO.2015.7282379