Paper Abstract and Keywords |
Presentation |
2015-12-03 09:25
Deep Auto-encoder based Low-dimensional Feature Extraction using FFT Spectral Envelopes in Statistical Parametric Speech Synthesis Shinji Takaki, Junichi Yamagishi (NII) SP2015-81 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In the state-of-the-art statistical parametric speech synthesis system, a speech analysis module, e.g. STRAIGHT spectral analysis, is generally used for obtaining accurate and stable spectral envelopes, and then low-dimensional acoustic features extracted from obtained spectral envelopes are used for training acoustic models. However, a spectral envelope estimation algorithm used in such a speech analysis module includes various processing derived from human knowledge. In this paper, we investigate a deep auto-encoder based, non-linear, data-driven and unsupervised low-dimensional feature extraction using FFT spectral envelopes for statistical parametric speech synthesis. Experimental results have shown that a text-to-speech synthesis system using a deep auto-encoder based low-dimensional feature extraction from FFT spectral envelopes is indeed a promising approach. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Statistical parametric speech synthesis / DNN / Deep Auto-encoder / Spectral envelope / Vocoder / / / |
Reference Info. |
IEICE Tech. Rep., vol. 115, no. 346, SP2015-81, pp. 99-104, Dec. 2015. |
Paper # |
SP2015-81 |
Date of Issue |
2015-11-25 (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) |
Notes on Review |
This article is a technical report without peer review, and its polished version will be published elsewhere. |
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SP2015-81 |
Conference Information |
Committee |
NLC IPSJ-NL SP IPSJ-SLP |
Conference Date |
2015-12-02 - 2015-12-04 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Nagoya Inst of Tech. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
The Second Natural Language Processing Symposium & The 17th Spoken Language Symposium |
Paper Information |
Registration To |
SP |
Conference Code |
2015-12-NLC-NL-SP-SLP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Deep Auto-encoder based Low-dimensional Feature Extraction using FFT Spectral Envelopes in Statistical Parametric Speech Synthesis |
Sub Title (in English) |
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Statistical parametric speech synthesis |
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DNN |
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Deep Auto-encoder |
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Spectral envelope |
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Vocoder |
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1st Author's Name |
Shinji Takaki |
1st Author's Affiliation |
National Institute of Informatics (NII) |
2nd Author's Name |
Junichi Yamagishi |
2nd Author's Affiliation |
National Institute of Informatics (NII) |
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Speaker |
Author-1 |
Date Time |
2015-12-03 09:25:00 |
Presentation Time |
25 minutes |
Registration for |
SP |
Paper # |
SP2015-81 |
Volume (vol) |
vol.115 |
Number (no) |
no.346 |
Page |
pp.99-104 |
#Pages |
6 |
Date of Issue |
2015-11-25 (SP) |
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