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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  
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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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Speaker
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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