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pro vyhledávání: '"Oura, Keiichiro"'
Autor:
Yoshimura, Takenori, Takaki, Shinji, Nakamura, Kazuhiro, Oura, Keiichiro, Hono, Yukiya, Hashimoto, Kei, Nankaku, Yoshihiko, Tokuda, Keiichi
This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. Since the mel-cepstral synthesis filter is explicitly embedded in neural waveform models in t
Externí odkaz:
http://arxiv.org/abs/2211.11222
Autor:
Nankaku, Yoshihiko, Sumiya, Kenta, Yoshimura, Takenori, Takaki, Shinji, Hashimoto, Kei, Oura, Keiichiro, Tokuda, Keiichi
This paper proposes a novel Sequence-to-Sequence (Seq2Seq) model integrating the structure of Hidden Semi-Markov Models (HSMMs) into its attention mechanism. In speech synthesis, it has been shown that methods based on Seq2Seq models using deep neura
Externí odkaz:
http://arxiv.org/abs/2108.13985
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 2803-2815, 2021
This paper presents Sinsy, a deep neural network (DNN)-based singing voice synthesis (SVS) system. In recent years, DNNs have been utilized in statistical parametric SVS systems, and DNN-based SVS systems have demonstrated better performance than con
Externí odkaz:
http://arxiv.org/abs/2108.02776
Autor:
Hono, Yukiya, Takaki, Shinji, Hashimoto, Kei, Oura, Keiichiro, Nankaku, Yoshihiko, Tokuda, Keiichi
We propose PeriodNet, a non-autoregressive (non-AR) waveform generation model with a new model structure for modeling periodic and aperiodic components in speech waveforms. The non-AR waveform generation models can generate speech waveforms parallell
Externí odkaz:
http://arxiv.org/abs/2102.07786
Autor:
Hono, Yukiya, Tsuboi, Kazuna, Sawada, Kei, Hashimoto, Kei, Oura, Keiichiro, Nankaku, Yoshihiko, Tokuda, Keiichi
This paper proposes a hierarchical generative model with a multi-grained latent variable to synthesize expressive speech. In recent years, fine-grained latent variables are introduced into the text-to-speech synthesis that enable the fine control of
Externí odkaz:
http://arxiv.org/abs/2009.08474
Autor:
Nakamura, Kazuhiro, Takaki, Shinji, Hashimoto, Kei, Oura, Keiichiro, Nankaku, Yoshihiko, Tokuda, Keiichi
The present paper describes singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized sing
Externí odkaz:
http://arxiv.org/abs/1910.11690
The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized si
Externí odkaz:
http://arxiv.org/abs/1904.06868
Publikováno v:
In Journal of Shoulder and Elbow Surgery March 2023 32(3):486-491
Akademický článek
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Publikováno v:
In Journal of Shoulder and Elbow Surgery August 2018 27(8):1357-1365