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Committee Date Time Place Paper Title / Authors Abstract Paper #
Kyoto Doshisha Univ. [Poster Presentation] Speech features obtained from similarities between the input and output of a DNN-based VAD.
Nozomi Shigaraki, Kei Yamamori (Kanazawa Univ.), Suci Dwijayanti (Sriwijaya Univ.), Masato Miyoshi (Kanazawa Univ.) EA2019-95
We have been studying Voice activity detection (VAD) using a deep neural network (DNN). Log power spectra (LPS) and Spee... [more] EA2019-95
EA, SIP, SP 2019-03-14
Nagasaki i+Land nagasaki (Nagasaki-shi) [Poster Presentation] Voice activity detection under high levels of noise using gated convolutional neural networks
Li Li, Koshino Yuki, Matsumoto Mitsuo, Makino Shoji (Univ. Tsukuba) EA2018-102 SIP2018-108 SP2018-64
This paper deals with voice activity detection (VAD) tasks under high-level noise environments where signal-to-noise rat... [more] EA2018-102 SIP2018-108 SP2018-64
(Joint) [detail]
Okinawa   [Poster Presentation] Adaptive beat noise estimation for FM radio in motor vehicle
Kosuke Hasada, Arata Kawamura, Youji Iiguni (Osaka Univ.) EA2017-155 SIP2017-164 SP2017-138
In FM-radio on motor vehicles, there exists an interference called as a beat noise which is caused by electric control u... [more] EA2017-155 SIP2017-164 SP2017-138
ET 2016-12-10
Osaka Kindai University Utterance Detection using Facial Image Combined with Voice Detection -- Partial System for Reading Activity Understanding in Japanese Text Presentation System --
Shuichi Tashiro, Shu Aoki, Kyota Aoki, Koji Harada (Utsunomiya Univ.) ET2016-70
The authors implemented the system which detects utterance sections using mouth motion and reading aloud voice. This sys... [more] ET2016-70
SP 2016-10-27
Shizuoka Shizuoka University. Voice Activity Detection Using Throat Microphone and Lavalier Microphone for Multi-Party Conversations
Yoshihiro Otaka, Takashi Tsunakawa, Masafumi Nishida, Masafumi Nishimura (Shizuoka Univ.) SP2016-43
For analyzing multi-party conversations, accurate identification of the speaker and speech segment is important. For mor... [more] SP2016-43
SP 2015-10-16
Hyogo Kobe Univ. Multi-modal speech recognition using deep bottleneck features
Satoshi Tamura (Gifu Univ), Hiroshi Ninomiya (Nagoya Univ), Norihide Kitaoka (Tokushima Univ), Shin Osuga (Aisin Seiki), Yurie Iribe (Aichi Prefectural Univ), Kazuya Takeda (Nagoya Univ), Satoru Hayamizu (Gifu Univ) SP2015-69
In this paper, we propose a novel multi-modal speech recognition method which uses speech and lip images, employing Deep... [more] SP2015-69
(Joint) [detail]
Kanagawa Tokyo Institute of Technology (Suzukakedai Campus) Investigation of Deep Neural Network and Cross-adaptation for Voice Activity Detection in Meeting Speech
Akihiro Nakadani (Shizuoka Univ.), Longbiao Wang (Nagaoka Univ. of Tech.), Atsuhiko Kai (Shizuoka Univ.) SP2014-107
In voice activity detection(VAD), performance largely decreases under the influence of noise and reverberation. In this ... [more] SP2014-107
EA 2014-12-12
Ishikawa Satellite Plaza of Kanazawa University [Poster Presentation] Study on signal to noise ratio estimation based on optimal design of subband voice activity detection
Shota Morita (JAIST), Xugang Lu (NICT), Masashi Unoki (JAIST) EA2014-46
Estimation of signal to noise ratio (SNR) of speech plays an important role of noise reduction and speech intelligibilit... [more] EA2014-46
SP, IPSJ-MUS 2014-05-25
Tokyo   Modulation transfer function based robust method of voice activity detection for noisy reverberant environments -- Utilization of subband SNR estimation --
Shota Morita, Masashi Unoki (JAIST), Xugang Lu (NICT), Masato Akagi (JAIST) SP2014-41
Most of the current voice activity detection (VAD) algorithms deal with clean speech or additive noisy speech. However, ... [more] SP2014-41
SP 2013-02-28
Aichi Daido University [Poster Presentation] Comparison of classification methods for multi-modal voice activity detection
Hiroya Okuda, Satoshi Tamura, Satoru Hayamizu (Gifu Univ.) SP2012-124
Automatic Speech Recognition (ASR) technology has been developed and used in various situations, such as car navigation ... [more] SP2012-124
EMM 2013-01-29
Miyagi Tohoku Univ. Multi-modal Information Processing by Embedding Image Features into Speech Signal
Yohei Abe, Akinori Ito (Tohoku Univ.) EMM2012-91
Lip movement has a close relationship with speech because lip moves when we talk. The idea of this work is to extract th... [more] EMM2012-91
SP, IPSJ-SLP 2012-12-20
Tokyo TITECH(Ookayama) Recent efforts for high-performance multi-modal speech recognition
Satoshi Tamura, Peng Shen, Hiroya Okuda, Naoya Ukai, Takuya Kawasaki, Takumi Seko, Satoru Hayamizu (Gifu Univ.) SP2012-88
Regarding Multi-Modal Automatic Speech Recognition (MMASR) which uses acoustic and lip/mouth information, this paper des... [more] SP2012-88
SIS 2012-12-13
Chiba Nihon University Tsudanuma Campus Robust Speech Recognition for Plosive Sound under Noisy Environment
Yusuke Hashimoto, Wataru Takahashi, Yoshikazu Miyanaga (Hokkaido Univ.) SIS2012-37
In this papar, we propose robust speech recognition for plosive sounds under noisy environment.
The proposed method emp... [more]
Yamagata Hotel Takinoyu (Yamagata Pref.) Voice activity detection using density ratio estimation of speech and noise
Yuuki Tachioka, Toshiyuki Hanazawa, Tomohiro Narita, Jun Ishii (Mitsubishi Electric Co.) SP2012-54
In this paper, we propose a robust voice activity detection (VAD) method that uses a density ratio model. For VAD under ... [more] SP2012-54
EA, SP, SIP 2012-05-24
Osaka Osaka Univ. Nakanoshima Center Development of Robust Voice Activity Detection using Empirical Mode Decomposition and Modulation Spectrum Analysis
Yasuaki Kanai, Masashi Unoki (JAIST) EA2012-1 SIP2012-1 SP2012-1
Voice activity detection (VAD) is used to detect speech/non—speech periods in observed signals. However, current V... [more] EA2012-1 SIP2012-1 SP2012-1
EA, SP, SIP 2012-05-24
Osaka Osaka Univ. Nakanoshima Center Voice activity detection in MTF-based power envelope restoration
Masashi Unoki (JAIST), Xugang Lu (NICT), Rico Petrick (TUD), Shota Morita, Masato Akagi (JAIST), Ruediger Hoffmann (TUD) EA2012-2 SIP2012-2 SP2012-2
This paper reports comparative evaluations of conventional voice activity detection (VAD) methods in reverberant environ... [more] EA2012-2 SIP2012-2 SP2012-2
EA, SP, SIP 2012-05-24
Osaka Osaka Univ. Nakanoshima Center A study of acoustic distance measurement method based on interference of speech presented by a dialogue system in real environments
Masato Nakayama (Ritsumeikan University), Yuma Neki, Noboru Nakasako (Kinki Univ.), Tetsuji Uebo (WIRE AUTOMATIC DEVICE CO., LTD), Takanobu Nishiura (Ritsumeikan University) EA2012-3 SIP2012-3 SP2012-3
In this paper, we propose an acoustic distance measurement method based on interference of speech presented by a dialogu... [more] EA2012-3 SIP2012-3 SP2012-3
EA, SP, SIP 2012-05-24
Osaka Osaka Univ. Nakanoshima Center DSP Implementation of Noise Suppression Method in a Noisy Factory Environment
Hiromasa Terashima, Hidesumi Moriya, Takahiro Natori (Tokyo Univ. of Science, Suwa), Masahide Wakamiko (MICRON SEIKO Co., Ltd.), Nari Tanabe (Tokyo Univ. of Science, Suwa), Toshihiro Furukawa (Tokyo Univ. of Science) EA2012-7 SIP2012-7 SP2012-7
We presents a noise suppression method for noisy factory environment. The proposed algorithm (Step 1)determine the voise... [more] EA2012-7 SIP2012-7 SP2012-7
EA, SP, SIP 2012-05-25
Osaka Osaka Univ. Nakanoshima Center On Single Voice Activity Detection for 2 Channel Blind Source Separation
Syohei Ashikari, Arata Kawamura, Youji Iiguni (Osaka Univ) EA2012-25 SIP2012-25 SP2012-25
Degenerate Unmixing Estimation Technique (DUET) is a technique for blind source separation with two microphones.
DUET i... [more]
EA2012-25 SIP2012-25 SP2012-25
SS 2012-03-13
Okinawa Tenbusu-Naha Handsfree Voice Interface for Home Network Service Using a Microphone Array Network
Shimpei Soda, Masahide Nakamura, Shinsuke Matsumoto, Noriyuki Matsubara, Koji Kugata, Shintaro Izumi, Hiroshi Kawaguchi, Masahiko Yoshimoto (Kobe Univ.) SS2011-69
The voice control is a promising user interface for the home network system (HNS). In our previous interface, a user had... [more] SS2011-69
 Results 1 - 20 of 37  /  [Next]  
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