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| Presentation | 2021-10-19 15:10 A study on model training for DNN-HSMM-based speech synthesis using a large-scale speech corpus Nobuyuki Nishizawa, Gen Hattori (KDDI Research)  SP2021-34 WIT2021-27 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this study, an investigation into model training for DNN-HSMM-based speech synthesis using a large speech corpus collected for connection synthesis was conducted. While conventional HSMM-based speech synthesis uses decision trees to predict the HSMM parameters corresponding to the linguistic information, DNN-HSMM-based speech synthesis uses DNNs for this prediction. Thus, it is expected to synthesize higher quality sounds by the method. However, since the parameters of the state duration distributions of the HSMMs are simultaneously estimated by the training, the training by the stochastic gradient method may not properly progress in the early stage of model training where the states cannot be appropriately aligned with training data yet. In particular, the behavior of training of RNNs using LSTM (long short-term memory) for DNN-HSMM-based speech synthesis has not yet been sufficiently studied. The experimental results show that the model can be trained from the randomly initialized states by setting the learning rate of the optimizer appropriately, and the training data size at which performance of the prediction saturates is more than 20.6 hours where using a three-layer bidirectional RNN where each layer consists of 2048-cell LSTMs. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | DNN-HSMM-based speech synthesis / hidden semi-Marcov models / large-scale speech corpus / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 202, SP2021-34, pp. 52-57, Oct. 2021. | |
| Paper # | SP2021-34 | |
| Date of Issue | 2021-10-12 (SP, WIT) | |
| ISSN | 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 | SP2021-34 WIT2021-27 |
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| Conference Information | |
| Committee | SP WIT IPSJ-SLP ASJ-H |
| Conference Date | 2021-10-19 - 2021-10-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SP |
| Conference Code | 2021-10-SP-WIT-SLP-H |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A study on model training for DNN-HSMM-based speech synthesis using a large-scale speech corpus |
| Sub Title (in English) | |
| Keyword(1) | DNN-HSMM-based speech synthesis |
| Keyword(2) | hidden semi-Marcov models |
| Keyword(3) | large-scale speech corpus |
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| 1st Author's Name | Nobuyuki Nishizawa |
| 1st Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
| 2nd Author's Name | Gen Hattori |
| 2nd Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
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| Speaker | Author-1 |
| Date Time | 2021-10-19 15:10:00 |
| Presentation Time | 120 minutes |
| Registration for | SP |
| Paper # | SP2021-34, WIT2021-27 |
| Volume (vol) | vol.121 |
| Number (no) | no.202(SP), no.203(WIT) |
| Page | pp.52-57 |
| #Pages | 6 |
| Date of Issue | 2021-10-12 (SP, WIT) |
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