Spoken document retrieval with unsupervised query modeling techniques

Berlin Chen, Kuan Yu Chen, Pei Ning Chen, Yi Wen Chen

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Ever-increasing amounts of publicly available multimedia associated with speech information have motivated spoken document retrieval (SDR) to be an active area of intensive research in the speech processing community. Much work has been dedicated to developing elaborate indexing and modeling techniques for representing spoken documents, but only little to improving query formulations for better representing the information needs of users. The latter is critical to the success of a SDR system. In view of this, we present in this paper a novel use of a relevance language modeling framework for SDR. It not only inherits the merits of several existing techniques but also provides a principled way to render the lexical and topical relationships between a query and a spoken document. 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. In addition, 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. Empirical evaluations performed on the TDT (Topic Detection and Tracking) collections reveal that the methods derived from our modeling framework hold good promise for SDR and are very competitive with existing retrieval methods.

Original languageEnglish
Article number6239571
Pages (from-to)2602-2612
Number of pages11
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume20
Issue number9
DOIs
Publication statusPublished - 2012 Sep 7

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Keywords

  • Query modeling
  • relevance class
  • spoken document retrieval
  • topic modelling

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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