| Project/Area Number |
21300060
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Chiba University |
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Principal Investigator |
KUROIWA Shingo 千葉大学, 融合科学研究科(研究院), 教授 (20333510)
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| Co-Investigator(Kenkyū-buntansha) |
TSUGE Satoru 大同大学, 情報学部, 准教授 (00325250)
OSANAI Takashi 科学警察研究所, 法科学第四部, 部付主任研究官 (70392264)
SHINOZAKI Takahiro 東京工業大学, 総合理工学研究科, 准教授 (80447903)
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| Co-Investigator(Renkei-kenkyūsha) |
HORIUCHI Yasuo 千葉大学, 大学院・融合科学研究科, 准教授 (30272347)
NISHIDA Masafumi 同志社大学, 理工学部, 准教授 (80361442)
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| Project Period (FY) |
2009-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2013: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2012: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2011: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2010: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2009: ¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
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| Keywords | 音声学 / 話者認識 / 話者照合 / 話者識別 / 音声データベース / 法科学 / 話者内音声変動 / 『AWA長期間収録音声コーパス』 / 長期間収録音声コーパス / AWA-LTR / 音声特徴量 / SVM / 音声認証 / 話者内変 / 順位統計量 / 識別学習 / 話者内変動 |
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| Research Abstract |
This research project aimed to build a new speech corpus that enables many researchers to investigate changes in human voices during a day, a month or several years, and to develop accurate and robust speaker recognition methods for industrial and forensic uses. The speech corpus named "AWA Long-Term Recorded Speech Corpus (AWA-LTR), which is released by Speech Resources Consortium of National Institute of Informatics (NII-SRC), consists of 6 speaker's read speech data recorded at morning, noon, and evening every week for several years (2 to 10 years). Using this corpus, we have developed intra-speaker variability compensation methods that improve the robustness of speaker recognition techniques. We also studied effective speech features for forensic speaker recognition, a comparison between human and machine speaker recognition abilities, accurate and robust speaker modeling methods and speaker verification methods.
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