| Project/Area Number |
06452397
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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 |
Intelligent informatics
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| Research Institution | University of Tokyo |
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Principal Investigator |
HIROSE Keikichi University of Tokyo, Dept. of Inf. and Commu. Eng., Professor, 大学院・工学系研究科, 教授 (50111472)
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| Co-Investigator(Kenkyū-buntansha) |
OHNO Sumio Tokyo Science University, Dept. of Applied Electronics, Assistant, 基礎工学部, 助手 (80256677)
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| Project Period (FY) |
1994 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥7,500,000 (Direct Cost: ¥7,500,000)
Fiscal Year 1996: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 1995: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1994: ¥4,600,000 (Direct Cost: ¥4,600,000)
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| Keywords | Prosodic Features / Continuous Speech Recognition / Fundamental Frequency Contour / Superpositional Model / Syntactic Boundary / Accent Type / Statistical Model with Moraic Transition / Baysian Predictive Classification / フレーズ成分 / 文節境界 / 韻律規則 / 部分Abs / フレーズ境界 / HMM / 韻津的特徴 / 時間構造 / 部分AbS法 |
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| Research Abstract |
Prosodic feature-based methods were developed for word identification, syntactic boundary detection, and so on. These methods were utillized to aid continuous speech recognition. Followings are the major results.
1. Prosodic rules for read speech were modified and improved. Prosodic rules for dialogue speech were also construcled based on the comparative study with read speech. Prosodic features were clarified for speakers' intention, attitude and emotion.
2. A method was develped to detect syntactic boundaries in continuous speech using fundamental frequency contours and their macroscopic features.
3. A method was develped to extract phrase component onsels from fundamental frequency contours by suppressing local undulations due to accent components. By combining this with AbS method based on the superpositional model, automatic extraction of fundamental frequency contour features was realized and was applied to important word detection successfully.
4. A method was develped to estimate t
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he feasibility of recognition candidates where a fundamental frequency contour was generated for each candidate, and was compared to the observed contour. The method was shown to be effective in detecting recognition error accompanied by accent type changes and/or syntactic boundary changes.
5. A method was develped to model fundamental frequency contours statistically after representing them with several codes in moraic unit. The method was proved to be able to detect syntactic boundaries and to recognize accent types effectively.
6. A method was develped to divide training data phoneme HMM into several clusters by inspecting HMM path of each data. By arranging a new HMM for each cluster, recognition rate was clearly shown to increase.
7. A robust speech recognition method was developed based on Viterbi Baysian predictive classification. Validity of the method was shown with word recognition experiments under noisy conditions, where more than 10% improvement was observed as compared to conventional methods.
After incorporating the developed methods above into a continuous speech recognition system, and their positive effects on recognition were proved. Less
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