Improved speech summarization with multiple-hypothesis representations and Kullback-Leibler divergence measures

Shih Hsiang Lin, Berlin Chen

研究成果: 雜誌貢獻Conference article

20 引文 斯高帕斯(Scopus)

摘要

Imperfect speech recognition often leads to degraded performance when leveraging existing 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 new summarization method stemming from the Kullback-Leibler (KL) divergence measure and exploring both the sentence and document relevance information is proposed to work with such robust representations. Experiments on broadcast news speech summarization seem to demonstrate the utility of the presented approaches.

原文英語
頁(從 - 到)1847-1850
頁數4
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態已發佈 - 2009 十一月 26
事件10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, 英国
持續時間: 2009 九月 62009 九月 10

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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