Paper Abstract and Keywords |
Presentation |
2020-03-03 09:00
A Study for HMM-based embedded speech synthesis using a large-scale speech corpus Nobuyuki Nishizawa, Tomohiro Obara, Hiromi Ishizaki (KDDI Research, Inc.) EA2019-141 SIP2019-143 SP2019-90 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This study shows that our speech synthesis system based on HMM speech synthesis for embedded devices can perform real-time processing even with a large model HMM trained from a large-scale speech corpus of 59.3 hours. We have already proposed a speech synthesis system for a microcontroller (MCU) in which reduction of the computational costs and required RAM size are achieved by a fast waveform generation method and block processing of parameter generation processing, respectively. Since the size of the HMMs logarithmically affects the computational cost and the size is less dependent on processing other than the decision tree processing, a large-sized HMM for high-quality speech sounds can be adopted where a large flash memory is useable. The results of the experiment of this study shows that the processing speed of our system is sufficient for the real-time processing even where the large HMM about four times the size of the conventional one is used. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
HMM-based speech synthesis / large-scale speech corpus / embedded systems / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 441, SP2019-90, pp. 231-236, March 2020. |
Paper # |
SP2019-90 |
Date of Issue |
2020-02-24 (EA, SIP, 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) |
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EA2019-141 SIP2019-143 SP2019-90 |
Conference Information |
Committee |
SP EA SIP |
Conference Date |
2020-03-02 - 2020-03-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinawa Industry Support Center |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SP |
Conference Code |
2020-03-SP-EA-SIP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study for HMM-based embedded speech synthesis using a large-scale speech corpus |
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HMM-based speech synthesis |
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large-scale speech corpus |
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embedded systems |
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1st Author's Name |
Nobuyuki Nishizawa |
1st Author's Affiliation |
KDDI Research, Inc. (KDDI Research, Inc.) |
2nd Author's Name |
Tomohiro Obara |
2nd Author's Affiliation |
KDDI Research, Inc. (KDDI Research, Inc.) |
3rd Author's Name |
Hiromi Ishizaki |
3rd Author's Affiliation |
KDDI Research, Inc. (KDDI Research, Inc.) |
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Speaker |
Author-1 |
Date Time |
2020-03-03 09:00:00 |
Presentation Time |
90 minutes |
Registration for |
SP |
Paper # |
EA2019-141, SIP2019-143, SP2019-90 |
Volume (vol) |
vol.119 |
Number (no) |
no.439(EA), no.440(SIP), no.441(SP) |
Page |
pp.231-236 |
#Pages |
6 |
Date of Issue |
2020-02-24 (EA, SIP, SP) |
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