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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 20  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
SIP, SP, EA, IPSJ-SLP [detail] 2024-02-29
16:45
Okinawa
(Primary: On-site, Secondary: Online)
Multiple Lag Window Pairs for Estimation of Fundamental Frequency and Periodicity Measure of Speech Signals
Michiki Koshimori (UEC), Shigeki Sagayama (UTokyo/UEC), Toru Nakashika (UEC) EA2023-75 SIP2023-122 SP2023-57
Extending the main concept of modified autocorrelation method in LPC, we investigate lag windows, lag window pairs, and ... [more] EA2023-75 SIP2023-122 SP2023-57
pp.85-90
SIP, SP, EA, IPSJ-SLP [detail] 2024-03-01
10:40
Okinawa
(Primary: On-site, Secondary: Online)
Speech representation based on VAE assuming gamma distribution for latent variables and observation
Nanako Imaichi, Toru Nakashika (UEC) EA2023-104 SIP2023-151 SP2023-86
Recently, deep generative models that can represent complex relationships in data generation have been attracting attent... [more] EA2023-104 SIP2023-151 SP2023-86
pp.256-261
SIP, SP, EA, IPSJ-SLP [detail] 2024-03-01
10:40
Okinawa
(Primary: On-site, Secondary: Online)
An Investigation on the Speech Recovery from EEG Signals Using Transformer
Tomoaki Mizuno (The Univ. of Electro-Communications), Takuya Kishida (Aichi Shukutoku Univ.), Natsue Yoshimura (Tokyo Tech), Toru Nakashika (The Univ. of Electro-Communications) EA2023-108 SIP2023-155 SP2023-90
Synthesizing full speech from ElectroEncephaloGraphy(EEG) signals is a challenging task. In this paper, speech reconstru... [more] EA2023-108 SIP2023-155 SP2023-90
pp.277-282
SP, IPSJ-MUS, IPSJ-SLP [detail] 2023-06-23
13:50
Tokyo
(Primary: On-site, Secondary: Online)
Impression Conversion of Speech for Unknown Speakers Using FaderNet
Saki Kugimoto, Toru Nakashika (UEC) SP2023-2
This paper proposes a model that can convert impressions of unknown speakers who do not have impression labels, based on... [more] SP2023-2
pp.4-7
SP, IPSJ-MUS, IPSJ-SLP [detail] 2023-06-24
13:50
Tokyo
(Primary: On-site, Secondary: Online)
[Short Paper] SBERT-based Musical Components Estimation from Lyrics Trained with Imbalanced "Orpheus" Data
Mastuti Puspitasari, Takuya Takahashi (UEC), Gen Hori (AU), Shigeki Sagayama, Toru Nakashika (UEC) SP2023-18
This research was done to develop neural models that are capable of estimating appropriate musical components based on l... [more] SP2023-18
pp.86-90
SP, IPSJ-MUS, IPSJ-SLP [detail] 2023-06-24
13:50
Tokyo
(Primary: On-site, Secondary: Online)
Non-chord Tone Data Collection for Music Analysis and Generation
Takuya Takahashi, , Toru Nakashika, Shigeki Sagayama (UEC) SP2023-20
The non-chord tones are one of the components of harmony theory and play an important role in music analysis and composi... [more] SP2023-20
pp.97-102
SP, IPSJ-SLP, EA, SIP [detail] 2023-03-01
10:40
Okinawa
(Primary: On-site, Secondary: Online)
Diffusion-based parallel voice conversion with source-feature condition
Takuya Kishida, Toru Nakashika (UEC) EA2022-107 SIP2022-151 SP2022-71
We propose a voice conversion method based on a diffusion probabilistic model trained on a parallel dataset. Since the d... [more] EA2022-107 SIP2022-151 SP2022-71
pp.191-196
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-17
15:00
Online Online Blind Source Separation based on Independent Low-Rank Matrix Analysis using Restricted Boltzmann Machines
Shotaro Furuta, Takuya Kishida, Toru Nakashika (UEC) SP2022-8
In this paper, we propose a new blind source separation method that combines independent low-rank source separation (ILR... [more] SP2022-8
pp.26-29
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-18
15:00
Online Online Improved speech analysis using F0-adaptive lag window
Michiki Koshimori, Shigeki Sagayama, Takuya Kishida, Toru Nakashika (UEC) SP2022-21
The lag window method is based on a source-filter model, which separates the source information from the filter informat... [more] SP2022-21
pp.90-93
SP, IPSJ-MUS, IPSJ-SLP [detail] 2022-06-18
15:00
Online Online VAE-VC based on cross-entropy error minimization of LSP frequency intervals
Yoshihiro Hiramoto, Shigeki Sagayama, Takuya Kishida, Toru Nakashika (UEC) SP2022-23
 [more] SP2022-23
pp.100-103
SP, IPSJ-SLP, IPSJ-MUS 2021-06-19
15:00
Online Online Unseen speaker's Voice Conversion by FaderNetVC with Speaker Feature Extractor
Takumi Isako, Takuya Kishida, Toru Nakashika (UEC) SP2021-20
In recent years, many voice conversion models using Deep Neural Network (DNN) have been proposed, and FaderNetVC is one ... [more] SP2021-20
pp.91-96
SP, SIP, EA 2017-03-02
13:10
Okinawa Okinawa Industry Support Center Feature Extraction Using Adaptive Restricted Boltzmann Machine for Dysarthric Speech Recognition
Yuki Takashima (Kobe Univ.), Toru Nakashika (UEC), Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) EA2016-140 SIP2016-195 SP2016-135
 [more] EA2016-140 SIP2016-195 SP2016-135
pp.321-326
NLC, IPSJ-NL, SP, IPSJ-SLP
(Joint) [detail]
2015-12-02
10:25
Aichi Nagoya Inst of Tech. Simultaneous Modelling of Acoustic, Phonetic, Speaker Features Using Improved Three-Way Restricted Boltzmann Machine
Toru Nakashika (UEC), Tetsuya Takiguchi (Kobe Univ.) SP2015-71
In this paper, we argue the way of modelling speech signals using improved three-way restricted Boltzmann machine (3WRBM... [more] SP2015-71
pp.7-12
WIT, SP, ASJ-H, PRMU 2015-06-18
15:15
Niigata   Phone Labeling Based on Gaussian Mixture Model for Dysarthric Speech Recognition
Yuki Takashima (Kobe Univ.), Toru Nakashika (UEC), Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) PRMU2015-44 SP2015-13 WIT2015-13
We investigate in this paper speech recognition for a person with an articulation disorder resulting from athetoid cereb... [more] PRMU2015-44 SP2015-13 WIT2015-13
pp.71-76
NLC, IPSJ-NL, SP, IPSJ-SLP, JSAI-SLUD
(Joint) [detail]
2014-12-16
13:30
Kanagawa Tokyo Institute of Technology (Suzukakedai Campus) [Poster Presentation] Voice Conversion Using Speaker Adapted Restricted Boltzmann Machine
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-126
(Advance abstract in Japanese is available) [more] SP2014-126
pp.165-170
SP, IPSJ-MUS 2014-05-25
11:30
Tokyo   A joint restricted Boltzmann machine for dictionary learning in sparse-representation-based voice conversion
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-34
In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relati... [more] SP2014-34
pp.343-348
SP, IPSJ-SLP 2013-12-19
17:15
Tokyo   Speaker-dependent conditional restricted Boltzmann machine for voice conversion
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2013-88
In this paper, we present a voice conversion (VC) method that utilizes conditional restricted Boltzmann machines (CRBMs)... [more] SP2013-88
pp.83-88
SP 2011-07-23
09:55
Hokkaido Jozankei Grand Hotel Constrained Spectrum Generation for Mixed Sound Analysis Based on Probabilistic Spectrum Envelope
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2011-50
NMF (Non-negative matrix factorization) has been one of the most widely-used techniques for signal analysis in recent ye... [more] SP2011-50
pp.51-56
NLC, SP
(Joint) [detail]
2010-12-21
16:40
Tokyo National Olympics Memorial Youth Center Iterative basis generation and supervised non-negative matrix factorization for signal analysis
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) NLC2010-29 SP2010-102
NMF (Non-negative Matrix Factorization) has been one of the most useful techniques for signal analysis in recent years.
... [more]
NLC2010-29 SP2010-102
pp.195-200
SP, NLC 2009-12-22
15:50
Tokyo Univ. of Tokyo A study on speech synthesis by modeling harmonics structure with Multi Beta Mixture Model
Toru Nakashika (Kobe Univ.), Ryuki Tachibana, Masafumi Nishimura (IBM Japan), Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) NLC2009-26 SP2009-90
There are currently some researches related to speech synthesis, but here we present a new framework
for speech synthes... [more]
NLC2009-26 SP2009-90
pp.165-170
 Results 1 - 20 of 20  /   
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