Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
EA, SIP, SP, IPSJ-SLP [detail] |
2022-03-01 14:20 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Fast Distortion Pedal Modeling with Fine-Tuning Haruki Shoji, Kento Yoshimoto, Daiki Saka, Hiroki Kuroda, Daichi Kitahara, Kenichiro Tanaka, Akira Hirabayashi (Ritsumeikan Univ.) EA2021-75 SIP2021-102 SP2021-60 |
We propose a fast modeling method for distortion pedals based on deep learning. For modeling many times with different p... [more] |
EA2021-75 SIP2021-102 SP2021-60 pp.70-75 |
EA, US (Joint) |
2021-12-22 13:30 |
Kumamoto |
Sojo University |
[Poster Presentation]
Improved voice quality due to multi-speaker learning with WaveNet vocoder Satoshi Yoshida, Shingo Uenohara, Ken'ichi Furuya (Oita Univ.) EA2021-57 |
In recent years, speech synthesis and voice quality conversion techniques using neural networks have attracted much atte... [more] |
EA2021-57 pp.1-6 |
SP, EA, SIP |
2020-03-02 09:20 |
Okinawa |
Okinawa Industry Support Center (Cancelled but technical report was issued) |
Investigation of neural speech rate conversion with multi-speaker WaveNet vocoder Takuma Okamoto (NICT), Keisuke Matsubara (Kobe Univ./NICT), Tomoki Toda (Nagoya Univ./NICT), Yoshinori Shiga, Hisashi Kawai (NICT) EA2019-101 SIP2019-103 SP2019-50 |
Speech rate conversion technology, which can expand or compress speech waveforms without changing pitch of sound, is con... [more] |
EA2019-101 SIP2019-103 SP2019-50 pp.1-6 |
SP, EA, SIP |
2020-03-02 13:00 |
Okinawa |
Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
High-precision modeling of distortion stomp box by deep learning using spectral features Kento Yoshimoto, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.) EA2019-124 SIP2019-126 SP2019-73 |
We propose a method for modeling distortion stomp box with high accuracy using a deep neural network, WaveNet. The conve... [more] |
EA2019-124 SIP2019-126 SP2019-73 pp.135-140 |
NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2019-12-06 16:00 |
Tokyo |
NHK Science & Technology Research Labs. |
A comparison of neural vocoders in singing voice synthesis Sota Wada, Yukiya Hono, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2019-42 |
In this study, we compare five types of vocoders based on neural networks (neural vocoders) for singing voice synthesis.... [more] |
SP2019-42 pp.85-90 |
SP |
2019-08-28 14:40 |
Kyoto |
Kyoto Univ. |
[Poster Presentation]
An investigation on training of WaveNet vocoder in end-to-end text-to-speech Kazuki Yasuhara, Tomoki Hayashi, Tomoki Toda (Nagoya Univ.) SP2019-14 |
In this paper, we investigate the training of WaveNet vocoder in end-to-end text-to-speech. Tacotron 2, which is an end-... [more] |
SP2019-14 pp.31-36 |
SIP, EA, SP, MI (Joint) [detail] |
2018-03-20 09:00 |
Okinawa |
|
[Poster Presentation]
Development of NU Voice Conversion System 2018 Patrick Lumban Tobing, Yi-Chiao Wu, Tomoki Hayashi, Kazuhiro Kobayashi (Nagoya Univ.), Tomoki Toda (Nagoya Univ./JST PRESTO) EA2017-138 SIP2017-147 SP2017-121 |
This paper presents NU (Nagoya University) voice conversion (VC) system for the HUB task of Voice
Conversion Challenge ... [more] |
EA2017-138 SIP2017-147 SP2017-121 pp.203-208 |
SIP, EA, SP, MI (Joint) [detail] |
2018-03-20 09:00 |
Okinawa |
|
[Poster Presentation]
Do prosodic manual annotations matter for Japanese speech synthesis systems with WaveNet vocoder? Hieu-Thi Luong, Xin Wang, Junichi Yamagishi (NII), Nobuyuki Nishizawa (KDDI Research) EA2017-140 SIP2017-149 SP2017-123 |
We investigated the impact of noisy linguistics features on the performance of a Japanese neural net- work based speech ... [more] |
EA2017-140 SIP2017-149 SP2017-123 pp.215-220 |
SP, ASJ-H |
2018-01-21 14:45 |
Tokyo |
The University of Tokyo |
An investigation of multi-speaker WaveNet vocoder Tomoki Hayashi, Kazuhiro Kobayashi, Akira Tamamori, Kazuya Takeda, Tomoki Toda (Nagoya Univ.) SP2017-81 |
In this paper, we investigate a multi-speaker WaveNet vocoder. In our previous work, we have demonstrated that our propo... [more] |
SP2017-81 pp.81-86 |
SP, ASJ-H |
2018-01-21 15:35 |
Tokyo |
The University of Tokyo |
Mel-cepstrum based quantization noise shaping applied to speech synthesis based on WaveNet Takenori Yoshimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2017-83 |
This paper proposes a mel-cepstrum based quantization noise shaping for improving the quality of synthetic speech genera... [more] |
SP2017-83 pp.93-98 |
SP, ASJ-H |
2018-01-21 16:00 |
Tokyo |
The University of Tokyo |
A study on voice conversion based on WaveNet Jumpei Niwa, Takenori Yoshimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (NIT) SP2017-84 |
This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acous... [more] |
SP2017-84 pp.99-104 |
SP, SIP, EA |
2017-03-01 09:20 |
Okinawa |
Okinawa Industry Support Center |
Speech waveform synthesis based on WaveNet considering speech generation process Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda (Nagoya Univ.) EA2016-82 SIP2016-137 SP2016-77 |
Our aim is to realize a new vocoder, which can resolve various constraints imposed on source-filter model and deal with ... [more] |
EA2016-82 SIP2016-137 SP2016-77 pp.1-6 |