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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2023-06-24 13:50 |
Tokyo | (Primary: On-site, Secondary: Online) |
Domain adaptation of speech recognition models based on self-supervised learning using target domain speech Takahiro Kinouchi (TUT), Atsunori Ogawa (NTT), Yuko Wakabayashi, Norihide Kitaoka (TUT)  SP2023-19 |
In this study, we propose a domain adaptation method using only speech data in the target domain without using transcrib... [more] | SP2023-19 pp.91-96 |
| ET | 2023-03-14 14:10 |
Tokushima | Tokushima University (Primary: On-site, Secondary: Online) |
HMD-type customer service training support system using eye tracking Takeru Oue, Yukihiro Matsubara, Kousuke Mouri, Masaru Okamoto (Hiroshima City Univ.) ET2022-71 |
In this paper, customer service training support system using HMD and eye tracking approach are developed. By using this... [more] | ET2022-71 pp.73-78 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 09:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Study on Scheduled Sampling for Neural Transducer-based ASR Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Kohei Matsuura, Tomohiro Tanaka, Ryo Masumura (NTT) EA2022-100 SIP2022-144 SP2022-64 |
In this paper, we propose scheduled sampling approaches suited for the recurrent neural network-transducer (RNNT) that i... [more] | EA2022-100 SIP2022-144 SP2022-64 pp.147-152 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 11:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
Representation and Prediction of Accent Phrase Prosodic Features in Japanese Text-to-Speech Masaki Sato, Shinnosuke Takamichi, Hiroshi Saruwatari (The Univ. of Tokyo) EA2022-108 SIP2022-152 SP2022-72 |
In order to use speech synthesis in a variety of situations such as dialogue systems and emotional expression in audiobo... [more] | EA2022-108 SIP2022-152 SP2022-72 pp.197-202 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 11:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
Analysis of Noisy-target Training for DNN-based speech enhancement and investigation towards its practical use Takuya Fujimura, Tomoki Toda (Nagoya Univ.) EA2022-112 SIP2022-156 SP2022-76 |
Deep neural network (DNN)-based speech enhancement usually uses a clean speech as a training target. However, it is hard... [more] | EA2022-112 SIP2022-156 SP2022-76 pp.221-226 |
| EA, US (Joint) |
2022-12-22 16:50 |
Hiroshima | Satellite Campus Hiroshima | [Poster Presentation]
Data augmentation method for machine learning on speech data Tsubasa Maruyama (Tokyo Tech), Tsutomu Ikegami (AIST), Toshio Endo (Tokyo Tech), Takahiro Hirofuchi (AIST) EA2022-68 |
In machine learning, data augmentation is a method to enhance the number and diversity of data by adding transformations... [more] | EA2022-68 pp.42-48 |
| NLC, IPSJ-NL, SP, IPSJ-SLP [detail] | 2022-11-30 15:30 |
Tokyo | (Primary: On-site, Secondary: Online) |
Semi-supervised joint training of text to speech and automatic speech recognition using unpaired text data Naoki Makishima, Satoshi Suzuki, Atsushi Ando, Ryo Masumura (NTT) NLC2022-14 SP2022-34 |
This paper presents a novel joint training of text to speech (TTS) and automatic speech recognition (ASR) with small amo... [more] | NLC2022-14 SP2022-34 pp.27-32 |
| NLC, IPSJ-NL, SP, IPSJ-SLP [detail] | 2022-12-01 15:20 |
Tokyo | (Primary: On-site, Secondary: Online) |
Domain and language adaptation of large-scale pretrained model for speech recognition of low-resource language Kak Soky (Kyoto University), Sheng Li (NICT), Chenhui Chu, Tatsuya Kawahara (Kyoto University) NLC2022-17 SP2022-37 |
The self-supervised learning (SSL) models are effective for automatic speech recognition (ASR). Due to the huge paramete... [more] | NLC2022-17 SP2022-37 pp.45-49 |
| NLC, IPSJ-NL, SP, IPSJ-SLP [detail] | 2022-12-01 15:50 |
Tokyo | (Primary: On-site, Secondary: Online) |
ASR model adaptation to target domain with large-scale audio data without transcription Takahiro Kinouchi, Daiki Mori (TUT), Ogawa Atsunori (NTT), Norihide Kitaoka (TUT) NLC2022-18 SP2022-38 |
Nowadays, speech recognition is used in various services and businesses thanks to the advent of high-performance models ... [more] | NLC2022-18 SP2022-38 pp.50-53 |
| SIP | 2022-08-26 14:08 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Study on Bone-conducted Speech Enhancement Using Vector-quantized Variational Autoencoder and Gammachirp Filterbank Cepstral Coefficients Quoc-Huy Nguyen, Masashi Unoki (JAIST) SIP2022-71 |
Bone-conducted (BC) speech potentially avoids the undesired effects on recorded speech due to background noise or reverb... [more] | SIP2022-71 pp.109-114 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 15:00 |
Online | Online | Representation and analytical normalization for vocal-tract-length transformation by group theory Atsushi Miyashita, Tomoki Toda (Nagoya Univ) SP2022-11 |
In automatic speech recognition, a recognition result should be invariant with respect to acoustic changes caused by dif... [more] | SP2022-11 pp.41-46 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-18 15:00 |
Online | Online | Unsupervised Training of Sequential Neural Beamformer Using Blindly-separated and Non-separated Signals Kohei Saijo, Tetsuji Ogawa (Waseda Univ.) SP2022-25 |
We present an unsupervised training method of the sequential neural beamformer (Seq-NBF) using the separated signals fro... [more] | SP2022-25 pp.110-115 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-01 12:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
Incorporating Acoustic and Textual Information for Language Modeling in Code-switching Speech Recognition Roland Hartanto, Kuniaki Uto, Koichi Shinoda (TokyoTech) EA2021-73 SIP2021-100 SP2021-58 |
People who speak two or more languages tend to alternate the language when they are speaking. This particular phenomenon... [more] | EA2021-73 SIP2021-100 SP2021-58 pp.56-63 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-02 15:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
A study of shout detection for clipped speech Taito Ishida, Kazuhiro Matsuda, Takahiro Fukumori, Yoichi Yamashita (Ritsumeikan Univ.) EA2021-97 SIP2021-124 SP2021-82 |
Recently, several audio surveillance systems using shouted speech have been proposed for safety in daily life. Although... [more] |
EA2021-97 SIP2021-124 SP2021-82 pp.207-212 |
| 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, WIT, IPSJ-SLP, ASJ-H [detail] | 2021-10-19 15:10 |
Online | Online | A study on model training for DNN-HSMM-based speech synthesis using a large-scale speech corpus Nobuyuki Nishizawa, Gen Hattori (KDDI Research) SP2021-34 WIT2021-27 |
In this study, an investigation into model training for DNN-HSMM-based speech synthesis using a large speech corpus coll... [more] | SP2021-34 WIT2021-27 pp.52-57 |
| SIS | 2021-03-04 09:00 |
Online | Online | Optimization source-filtere based speech waveform generation using adversarial training Hayato Mitsui, Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ.) SIS2020-35 |
This research aims to improve the accuracy of the source-filter based speech waveform generation model using deep learni... [more] | SIS2020-35 pp.1-4 |
| PRMU, IPSJ-CVIM | 2021-03-05 09:45 |
Online | Online | Improved Speech Separation Performance from Monaural Mixed Speech Based on Deep Embedding Network Shaoxiang Dang, Tetsuya Matsumoto, Hiroaki Kudo (Nagoya Univ.), Yoshinori Takeuchi (Daido Univ.) PRMU2020-85 |
Speech separation refers to the separation of utterances in which multiple people are speaking simultaneously. The idea ... [more] | PRMU2020-85 pp.91-96 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 14:05 |
Online | Online | [Poster Presentation]
A unified source-filter network for neural vocoder Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda (Nagoya Univ.) EA2020-69 SIP2020-100 SP2020-34 |
In this paper, we propose a method to develop a neural vocoder using a single network based on the source-filter theory.... [more] | EA2020-69 SIP2020-100 SP2020-34 pp.57-62 |
| MVE, IPSJ-CVIM | 2021-01-21 15:50 |
Online | Online | Customer service training VR system with spoken voice traning Toki Nishio, Soichiro Iida (Univ. of Tsukuba), Yuta Sano, Leow Chee Siang, Hiromitsu Nishizaki (Univ. of Yamanashi), Takehito Utsuro, Junichi Hoshino (Univ. of Tsukuba) MVE2020-32 |
A filler is a word that has no meaning in itself and is used to fill gaps in conversation. In customer service, this fil... [more] | MVE2020-32 pp.13-16 |
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