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.
|頁（從 - 到）||1847-1850|
|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 6 → 2009 九月 10
ASJC Scopus subject areas