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
Link To Document