Leveraging kullbackLeibler divergence measures and information-rich cues for speech summarization

Shih Hsiang Lin*, Yao Ming Yeh, Berlin Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Imperfect speech recognition often leads to degraded performance when exploiting conventional text-based methods for speech summarization. To alleviate this problem, this paper investigates various ways to robustly represent the recognition hypotheses of spoken documents beyond the top scoring ones. Moreover, a summarization framework, building on the KullbackLeibler (KL) divergence measure and exploring both the relevance and topical information cues of spoken documents and sentences, is presented to work with such robust representations. Experiments on broadcast news speech summarization tasks appear to demonstrate the utility of the presented approaches.

Original languageEnglish
Article number5549862
Pages (from-to)871-882
Number of pages12
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number4
Publication statusPublished - 2011


  • KullbackLeibler (KL) -divergence
  • multiple recognition hypotheses
  • relevance information
  • speech summarization
  • topical information

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Leveraging kullbackLeibler divergence measures and information-rich cues for speech summarization'. Together they form a unique fingerprint.

Cite this