K
Kazuya Takeda
Researcher at Nagoya University
Publications - 546
Citations - 9667
Kazuya Takeda is an academic researcher from Nagoya University. The author has contributed to research in topics: Speech processing & Speech enhancement. The author has an hindex of 42, co-authored 495 publications receiving 7719 citations. Previous affiliations of Kazuya Takeda include Kobe Women's University & Nara Institute of Science and Technology.
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Journal ArticleDOI
Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning
Kyle Sama,Yoichi Morales,Hailong Liu,Naoki Akai,Alexander Carballo,Eijiro Takeuchi,Kazuya Takeda +6 more
TL;DR: A method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors and results show that the autonomous agent built with the driving behaviors was capable of driving similarly to expert human drivers.
Proceedings ArticleDOI
Speech recognition based on space diversity using distributed multi-microphone
TL;DR: The experimental results show that the proposed space diversity speech recognition system can attain about 80% in accuracy, while the performances of conventional HMMs using close-talking microphones are less than 50%, indicating that the space diversity approach is promising for robust speech recognition under a real acoustic environment.
Journal ArticleDOI
Allergic conversion of protective mucosal immunity against nasal bacteria in patients with chronic rhinosinusitis with nasal polyposis.
Kazuya Takeda,Shuhei Sakakibara,Kazuo Yamashita,Daisuke Motooka,Shota Nakamura,Marwa Ali El Hussien,Jun Katayama,Yohei Maeda,Masanobu Nakata,Shigeyuki Hamada,Daron M. Standley,Masaki Hayama,Takashi Shikina,Hidenori Inohara,Hitoshi Kikutani +14 more
TL;DR: In this paper, the authors identify reactive allergens of IgE antibodies produced locally in NPs of patients with chronic rhinosinusitis with nasal polyposis (CRSwNP).
Proceedings ArticleDOI
Continuous point cloud data compression using SLAM based prediction
TL;DR: A new compression method using location and orientation information from Simultaneous Localization and Mapping (SLAM) can take advantage of the 3D characteristic of point cloud by a predicting process which simulating the working procedure of 3D LiDAR.
Journal ArticleDOI
Japanese Dictation Toolkit -1997 version
Tatsuya Kawahara,Akinobu Lee,Tetsunori Kobayashi,Kazuya Takeda,Nobuaki Minematsu,Katsunobu Itou,Akinori Ito,Mikio Yamamoto,Atsushi Yamada,Takehito Utsuro,Kiyohiro Shikano +10 more
TL;DR: The Japanese Dictation Toolkit has been designed and developed as a baseline platform for Japanese LVCSR (Large Vocabulary Continuous Speech Recognition), and implemented a baseline 5, 000-word dictation system and evaluated various components.