Constructing effective ranking models for speech summarization

Yueng Tien Lo, Shih Hsiang Lin, Berlin Chen

研究成果: 書貢獻/報告類型會議貢獻

2 引文 斯高帕斯(Scopus)

摘要

Speech summarization, facilitating users to better browse through and understand speech information (especially, spoken documents), has become an active area of intensive research recently. Many of the existing machine-learning approaches to speech summarization cast important sentence selection as a two-class classification problem and have shown empirical success for a wide array of summarization tasks. One common deficiency of these approaches is that the corresponding learning criteria are loosely related to the final evaluation metric. To cater for this problem, we present a novel probabilistic framework to learn the summarization models, building on top of the Bayes decision theory. Two effective training criteria, viz. maximum relevance estimation (MRE) and minimum ranking loss estimation (MRLE), deduced from such a framework are introduced to characterize the pair-wise preference relationships between spoken sentences. Experiments on a broadcast news speech summarization task exhibit the performance merits of our summarization methods when compared to existing methods.

原文英語
主出版物標題2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
頁面5053-5056
頁數4
DOIs
出版狀態已發佈 - 2012 十月 23
事件2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, 日本
持續時間: 2012 三月 252012 三月 30

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

其他

其他2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
國家日本
城市Kyoto
期間12/3/2512/3/30

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • 引用此

    Lo, Y. T., Lin, S. H., & Chen, B. (2012). Constructing effective ranking models for speech summarization. 於 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (頁 5053-5056). [6289056] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6289056