Speech indexing using semantic context inference

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

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)717-720
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2011 Dec 1
Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
Duration: 2011 Aug 272011 Aug 31

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation


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