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

Shih Hsiang Lin, Yao Ming Yeh, Berlin Chen

研究成果: 雜誌貢獻文章同行評審

19 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
文章編號5549862
頁(從 - 到)871-882
頁數12
期刊IEEE Transactions on Audio, Speech and Language Processing
19
發行號4
DOIs
出版狀態已發佈 - 2011 四月 6

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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