| Item type |
文献 / Documents(1) |
| 公開日 |
2022-04-21 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1186/s13636-021-00225-4 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1186/s13636-021-00225-4 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Text-to-speech system for low-resource language using cross-lingual transfer learning and data augmentation |
|
言語 |
en |
| 著者 |
Byambadorj, Zolzaya
西村, 良太
Ayush, Altangerel
オオタ, ケンゴ
北岡, 教英
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Deep learning techniques are currently being applied in automated text-to-speech (TTS) systems, resulting in significant improvements in performance. However, these methods require large amounts of text-speech paired data for model training, and collecting this data is costly. Therefore, in this paper, we propose a single-speaker TTS system containing both a spectrogram prediction network and a neural vocoder for the target language, using only 30 min of target language text-speech paired data for training. We evaluate three approaches for training the spectrogram prediction models of our TTS system, which produce mel-spectrograms from the input phoneme sequence: (1) cross-lingual transfer learning, (2) data augmentation, and (3) a combination of the previous two methods. In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We found that using both cross-lingual transfer learning and augmented data during training resulted in the most natural synthesized target speech output. We also compare single-speaker and multi-speaker training methods, using sequential and simultaneous training, respectively. The multi-speaker models were found to be more effective for constructing a single-speaker, low-resource TTS model. In addition, we trained two Parallel WaveGAN (PWG) neural vocoders, one using 13 h of our augmented data with 30 min of target language data and one using the entire 12 h of the original target language dataset. Our subjective AB preference test indicated that the neural vocoder trained with augmented data achieved almost the same perceived speech quality as the vocoder trained with the entire target language dataset. Overall, we found that our proposed TTS system consisting of a spectrogram prediction network and a PWG neural vocoder was able to achieve reasonable performance using only 30 min of target language training data. We also found that by using 3 h of target language data, for training the model and for generating augmented data, our proposed TTS model was able to achieve performance very similar to that of the baseline model, which was trained with 12 h of target language data. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Speech synthesis |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Text to speech |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Transfer learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Data augmentation |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Low-resource language |
| 書誌情報 |
en : EURASIP Journal on Audio, Speech, and Music Processing
巻 2021,
p. 42,
発行日 2021-12-04
|
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
16874722 |
| 出版者 |
|
|
出版者 |
BioMed Central |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Springer Nature |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| EID |
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|
識別子 |
384158 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
eurasip_2021_42.pdf (5.9 MB)
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