Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Reducing over-smoothness in speech synthesis using Generative Adversarial Networks. / Sheng, Leyuan; Pavlovskiy, Evgeniy N.
SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. стр. 972-974 8957862 (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Reducing over-smoothness in speech synthesis using Generative Adversarial Networks
AU - Sheng, Leyuan
AU - Pavlovskiy, Evgeniy N.
N1 - Publisher Copyright: © 2019 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/10
Y1 - 2019/10
N2 - Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-To-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech.
AB - Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-To-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech.
KW - GANs
KW - mel-spectrogram
KW - over-smoothness
KW - Speech synthesis
UR - http://www.scopus.com/inward/record.url?scp=85079077606&partnerID=8YFLogxK
U2 - 10.1109/SIBIRCON48586.2019.8957862
DO - 10.1109/SIBIRCON48586.2019.8957862
M3 - Conference contribution
AN - SCOPUS:85079077606
SN - 978-1-7281-4402-3
T3 - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
SP - 972
EP - 974
BT - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
Y2 - 21 October 2019 through 27 October 2019
ER -
ID: 28278090