Query modeling for spoken document retrieval

Berlin Chen*, Pei Ning Chen, Kuan Yu Chen

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

3 引文 斯高帕斯(Scopus)

摘要

Spoken document retrieval (SDR) has recently become a more interesting research avenue due to increasing volumes of publicly available multimedia associated with speech information. Many efforts have been devoted to developing elaborate indexing and modeling techniques for representing spoken documents, but only few to improving query formulations for better representing the users' information needs. In view of this, we recently presented a language modeling framework exploring a novel use of relevance information cues for improving query effectiveness. Our work in this paper continues this general line of research in two main aspects. We further explore various ways to glean both relevance and non-relevance cues from the spoken document collection so as to enhance query modeling in an unsupervised fashion. Furthermore, we also investigate representing the query and documents with different granularities of index features to work in conjunction with the various relevance and/or non-relevance cues. Experiments conducted on the TDT (Topic Detection and Tracking) SDR task demonstrate the performance merits of the methods instantiated from our retrieval framework when compared to other existing retrieval methods.

原文英語
主出版物標題2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings
頁面389-394
頁數6
DOIs
出版狀態已發佈 - 2011 十二月 1
事件2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 - Waikoloa, HI, 美国
持續時間: 2011 十二月 112011 十二月 15

出版系列

名字2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings

其他

其他2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011
國家/地區美国
城市Waikoloa, HI
期間2011/12/112011/12/15

ASJC Scopus subject areas

  • 人工智慧
  • 電腦視覺和模式識別
  • 人機介面

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