| Project/Area Number |
26280055
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perceptual information processing
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
能勢 隆 東北大学, 工学研究科, 准教授 (90550591)
Duh Kevin (DUH Kevin) 奈良先端科学技術大学院大学, 情報科学研究科, 助教 (80637322)
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| Co-Investigator(Renkei-kenkyūsha) |
ARAI Takayuki 上智大学, 理工学部・情報理工学科, 教授 (80266072)
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| Research Collaborator |
WATANABA Shinji
DUH Kevin
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥15,730,000 (Direct Cost: ¥12,100,000、Indirect Cost: ¥3,630,000)
Fiscal Year 2017: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2016: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2015: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2014: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
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| Keywords | 音声言語情報処理 / 深層学習 / モデル構造最適化 / 半教師あり学習 / 高性能音声認識システム / 強化学習 / ブラックボックス最適化 / 進化的アルゴリズム / 教師なし学習 / 教師なし適応 / 情報工学 / 音声等認識 / 音声認識 / ディープニューラルネットワーク / 音響モデル / 言語モデル / 音声合成 / 国際研究者交流（アメリカ） |
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| Outline of Final Research Achievements |
The purpose of this study is to make self-standing speech and language information processing systems that can learn from a small amount of labeled and a significant amount of unlabeled speech data as well as can automatically optimize its structure and learning conditions. We have proposed evolution strategy based automation method for neural network-based system development, series of semi-supervised learning methods for statistical speech models, and a reinforcement learning method of speech recognition systems. A high-performance Japanese speech recognition system integrating the research results have been published and widely used.
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