2004 Fiscal Year Final Research Report Summary
A study on content summarization for large spoken documents and content retrieval through spoken dialogue
| Project/Area Number |
13480095
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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 | Toyohashi University of Technology |
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Principal Investigator |
NAKAGAWA Seiichi Toyohashi University of Technology, Department of Information and Computer Sciences, 工学部, 教授 (20115893)
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| Co-Investigator(Kenkyū-buntansha) |
NITTA Tsuneo Toyohashi University of Technology, Department of Knowledge-based Information Engineering, 大学院・工学研究科, 教授 (70314101)
MASUYAMA Shigeru Toyohashi University of Technology, Department of Knowledge-based Information Engineering, 工学部, 教授 (60173762)
KITAOKA Norihide Toyohashi University of Technology, Department of Information and Computer Sciences, 工学部, 講師 (10333501)
KOBAYASHI Satoshi Shimane University, General Information Processing Center, 総合情報処理センター, 助教授 (90314096)
UTURO Takehito Kyoto University, Graduate School of Informatics, 情報学研究科, 講師 (90263433)
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| Project Period (FY) |
2001 – 2004
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| Keywords | Speech Database / Speech Recognition / Spoken Language / Speech Summarization / Speech Retrieval / Question-Answering / Dictation / Spoken Dialogue |
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| Research Abstract |
To develop an accurate large vocabulary continuous speech recognition system for spoken document retrieval in open domain, we proposed a search method using two search algorithms in parallel to achieve efficient and accurate decoding. We evaluated this new search algorithm and obtained significant improvement of recognition performance without severe increase of computational cost We also proposed to apply machine learning techniques to the task of combining outputs of multiple LVCSR models. The proposed technique had advantages over that by voting schemes such as ROVER, especially when the majority of participating models are not reliable. By using this technique, we performed a speech-driven Web retrieval task and improved speech recognition accuracy of spoken queries and then improved retrieval accuracy in speech driven Web retrieval We tried the summarization of spoken lectures. For this purpose, we investigated relations between linguistic surface information and human's results, and we obtained useful surface linguistic information. Next, we summarized spoken lectures based on this information, and compared them with human's results. As a result, we obtained a better F-measure and k-value comparable with human's results. We have developed a portable speech recognition module and an interpreter module in a spoken dialogue system. Furthermore, we also developed a dialogue strategy design tool, applied it to Mt.Fuji sightseeing guidance retrieval, literature retrieval and hotel reservation retrieval and then confirmed the usefulness.
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