| Project/Area Number |
22700193
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | National Institute of Information and Communications Technology |
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Principal Investigator |
LU Xugang 独立行政法人情報通信研究機構, ユニバーサルコミュニケーション研究所音声コミュニケーション研究室, 専攻研究員 (20362022)
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| Project Period (FY) |
2010 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2011: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2010: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | 知能ロボット / 再生核ヒルベルト空間 / 統計学習 / 音声信号処理 / 再生核空間の正規法 |
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| Research Abstract |
In most current speech feature extraction methods, only low-order statistical information is extracted. In order to improve the robustness, we proposed a speech signal processing method in a reproducing kernel Hilbert space(RKHS). We first proved a theoretical analysis of the proposed framework, and showed a connection of the trade-off problem in machine learning and noise reduction.(From theoretical aspect, we proved that the trade-off problem in machine learning and noise reduction is essentially the same). Based on the theoretical analysis, we applied the framework for speech enhancement, and voice activity detection problems. Based on our application experiments, we showed that the proposed framework can be well used on speech processing and obtained improvement compared with several traditional noise reduction methods.
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