Speech indexing using semantic context inference

Chien Lin Huang, Bin Ma, Haizhou Li, Chung-Hsien Wu

研究成果: Conference article同行評審

12 引文 斯高帕斯(Scopus)

摘要

This study presents a novel approach to spoken document retrieval based on semantic context inference for speech indexing. Each recognized term in a spoken document is mapped onto a semantic inference vector containing a bag of semantic terms through a semantic relation matrix. The semantic context inference vector is then constructed by summing up all the semantic inference vectors. Such a semantic term expansion and re-weighting make the semantic context inference vector a suitable representation for speech indexing. The experiments were conducted on 1550 anchor news stories collected from Mandarin Chinese broadcast news of 198 hours. The experimental results indicate that the proposed speech indexing using the semantic context inference contributes to a substantial performance improvement of spoken document retrieval.

原文English
頁(從 - 到)717-720
頁數4
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態Published - 2011 十二月 1
事件12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
持續時間: 2011 八月 272011 八月 31

All Science Journal Classification (ASJC) codes

  • 語言與語言學
  • 人機介面
  • 訊號處理
  • 軟體
  • 建模與模擬

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