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Paper Abstract and Keywords
Presentation 2018-01-20 13:25
A study on statistical speech synthesis based on GP-DNN hybrid model
Tomoki Koriyama, Takao Kobayashi (Tokyo Tech)
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a novel approach to Gaussian process regression (GPR)-based speech synthesis
in this paper.
Since the conventional GPR-based speech synthesis was based on data partition with a decision tree,
a decision tree was bottleneck of the performance of synthetic speech.
In contrast, we propose a hybrid model of Gaussian process and deep neural network (DNN).
In the hybrid model, DNN extracts context-derived features
and the output of DNN is used as an input of Gaussian process.
The parameters of DNN and GP are optimized using a minibatch-based
stochastic gradient descent method.
From the subjective evaluation results,
it can be seen that the proposed technique outperforms not only the conventional
GPR-based speech synthesis with decision trees
but also DNN-based speech synthesis.
Keyword (in Japanese) (See Japanese page) 
(in English) Gaussian process regression / stochastic variational inference / neural network / statistical parametric speech synthesis / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 393, SP2017-67, pp. 5-10, Jan. 2018.
Paper # SP2017-67 
Date of Issue 2018-01-13 (SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380

Conference Information
Committee SP ASJ-H  
Conference Date 2018-01-20 - 2018-01-21 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2018-01-SP-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on statistical speech synthesis based on GP-DNN hybrid model 
Sub Title (in English)  
Keyword(1) Gaussian process regression  
Keyword(2) stochastic variational inference  
Keyword(3) neural network  
Keyword(4) statistical parametric speech synthesis  
1st Author's Name Tomoki Koriyama  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Takao Kobayashi  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
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Date Time 2018-01-20 13:25:00 
Presentation Time 25 
Registration for SP 
Paper # IEICE-SP2017-67 
Volume (vol) IEICE-117 
Number (no) no.393 
Page pp.5-10 
#Pages IEICE-6 
Date of Issue IEICE-SP-2018-01-13 

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