IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

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) SP2017-67
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
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SP2017-67

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  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
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)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2018-01-20 13:25:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # SP2017-67 
Volume (vol) vol.117 
Number (no) no.393 
Page pp.5-10 
#Pages
Date of Issue 2018-01-13 (SP) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan