| Project/Area Number |
07558042
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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 | 展開研究 |
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| Research Field |
Intelligent informatics
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| Research Institution | Tohoku University |
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Principal Investigator |
MAKINO Shozo Tohoku Univ., Computer Center, Prof., 大型計算機センター, 教授 (00089806)
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| Co-Investigator(Kenkyū-buntansha) |
NIYADA Katsuyuki Matsushita Technology Institute Co., Researcher, 情報ネットワーク研究所, 研究職
CHEN Guo yue Tohoku Univ., Computer Center, Research Associ., 大型計算機センター, 助手 (20282014)
KUDOH Junichi Tohoku Univ., Computer Center, Associ.Prof., 大型計算機センター, 助教授 (40186408)
木幡 稔 東北大学, 工学研究科, 助教授 (30186720)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥5,300,000 (Direct Cost: ¥5,300,000)
Fiscal Year 1997: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1996: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1995: ¥3,700,000 (Direct Cost: ¥3,700,000)
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| Keywords | dictation system / Phoneme recognition / acquisition of language model / official reports / 連続音声認識 / モデル音声法 / 言語モデル / HMnet / 文節オートマトン / 解剖所見 / 識別学習 |
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| Research Abstract |
The experts often make official reports such as for estimation of real estiate, for medico-legal autopsy and so on. It is a time-consuming job to make official reports. If speech input is automatically transformed to sentenses, the load of making document will be decreased. In current speech input system, it is still difficult to automatically recognize continuously spoken general sentences. But, when the condition is so limited that a user is special, 1) a user is limited to a specified speaker, 2) the expression of document is almost decided, and the structure of sentence is comparatively simple, 3) the vocabulary number in the official reports is 4000 from 3000, and the vocabulary number decrease more by deviding the document into each of parts. These conditions will lighten load to a device, and the device will be developed with ease.
In this research, we have developed a sentense recognition system for autopsy reports. Firstly, we built an automaton by ECGI method to represent the structure of sentence. Then we defined the distance between the states of an automaton to strengthen correspondence to the words, the appearance of which was expected. Based on this definition, we developed a method in which an automaton was revised and generalized. For phoneme recognition, we used the model sound method developed by Niyada. We, all the members, put the above-mentioned methods together and made the sound input system of autopsy findings. The system ran to recognize sounds without time delay. Since the precision of sound recognition is not enough, the improvement of the system will be continued in future.
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